Devices, systems, and methods for adaptive health monitoring using behavioral, psychological, and physiological changes of a body portion

ABSTRACT

Devices, systems, and methods for monitoring musculoskeletal (MSK) health conditions of an individual, including joint flexibility, strength, and endurance as part of their overall care plan are described here. The overall system includes: a sensor that can be worn anywhere on the human body, an engaging app on a mobile-computing device, and software-based analytics and care management engine running on a cloud-computing infrastructure. The sensor is tuned to measure any human joint movement in any direction or axis as well as elevation and temperature. Methods performed by the various devices and systems and how it improves MSK health are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Nonprovisional patentapplication Ser. No. 16/755,399 filed Apr. 10, 2020, which is the U.S.National Stage filing of PCT Application Ser. No. PCT/US2018/055384filed Oct. 11, 2018, which claims the priority benefit of U.S.Provisional Patent Application Ser. No. 62/570,819, filed Oct. 11, 2017and U.S. Provisional Patent Application Ser. No. 62/699,286, filed Jul.17, 2018, the contents of each of which are herein incorporated byreference in their entireties.

TECHNICAL FIELD

This disclosure relates to the fields of health and wellness, and morespecifically, to devices, systems, and methods for digitally monitoringone or more health indicators of an individual, including positional,orientational, and circumferential changes to one or more body portions.

BACKGROUND

As people live longer, muscular skeletal health is a leading indicatorfor acute and chronic health conditions. Precise tracking and analysisof joint movement, gait and other aspects including, for example,increased circumference of a patient's limb, torso, waistline, or otherbody portion can enable the assessment and maintenance of overallwellness and assist in recovery from injuries, as well as assessment ofhealth before and after a surgical or injury episode and overall health.Additionally, monitoring other changes including a rapid increase in thecircumference of a patient's limb, torso, waistline, or other bodyportion can provide insight into other conditions that may or may noteffect general health or mobility, for example monitoring a rapidincrease in the circumference of a patient's leg may provide insightinto a swelling of the leg due to edema. Edema may be indicative of deepvein thrombosis, congestive heart failure, liver disease, kidneydisease, an allergic reaction, inflammation caused by injury orinfection, or other serious medical condition. Gradual increases in thecircumference of a waistline or other body portion may be due to weightgain, which itself may be indicative of inactivity, overeating,depression, a hormonal imbalance, or other medically-relevant condition.On the other hand, for some individuals, such as those being treated forcancer, pregnant women, undernourished individuals, and athletes, agradual increase in the circumference of a body portion may bedesirable, and may be indicative of healthy weight gain, a growingfetus, or an increase in muscle mass. In each of the above scenarios,monitoring any combination of movement, gait, and the circumference of abody portion may provide valuable insights into the health or wellnessof an individual.

Methods for monitoring can comprise adaptively providing a digital careplan and performing real-time monitoring and customized patient-specificfeedback on patient progression, performance, and adherence to the careprotocol. A care protocol can comprise methods for monitoring movementsof a patient's joints to assess and improve the muscular skeletal (MSK)health of a patient or user managing symptoms of a chronic condition,recovering from or preparing for a surgical procedure or as a means ofearly detection of health problems.

Current medical systems are incapable of collecting data about thepsychological and behavioral aspects of patients, which directly impactthe quality and speed of patient recovery. Furthermore, the disjointedroles of multiple healthcare providers result in a lack of integrationof information in a way that is useful and supportive of the patient intheir ongoing recovery outside of direct supervision. Finally, the lackof mechanisms that adapt to the needs and schedule of patients in theirdaily lives outside of supervised medical contexts limits the ability toprovide adequate recovery support and meaningfully enlist the support ofcritical influences like support of friends and family during criticalperiods of a patient's recovery. Disclosed herein a unified care systemcomprising adaptable content provided to the user, a care cloudcomprising systems and devices for on-going collection of data, andcommunication tools that facilitate and broker timely communicationbetween healthcare providers, the patient's support network (e.g.,family, friends, home care providers, etc.) and the patient.

SUMMARY

There is a need for improved means for monitoring musculoskeletal healthconditions of individuals (e.g., joint flexibility, strength, andendurance) as part of the health care regime or provider prescribed careplan. In particular, there is a need for devices, systems, and methodsthat can encourage individuals to engage in activities and habits thatboth improve overall health as well as assist in preparation for andrecovery from medical procedures or chronic conditions. There is a needfor devices, systems, and methods that can monitor and precisely trackand analyze joint movements and gait allowing overall assessment ofwellness and assist in health preparation before and after a surgical orinjury episode. There is also a need, more generally, for devices,systems, and methods that can: detect joint movements and gait andadaptively adjust a care protocol to the daily needs of the patient byproviding relevant health or fitness recommendations to an individual,and determine whether an individual has complied with therecommendations. The present disclosure is directed to devices, systems,algorithms, and methods that fill one or more of these needs.

One aspect of the disclosure is directed to a method for monitoringhealth parameters of an individual, including joint movements, gait,positional, orientational, and/or circumferential changes to a portionof a body. The method includes obtaining a plurality of measurements(e.g., circumferential, relative positon, position over time, etc.) ofthe body portion over a period of time via a sensor system, transmittingthe measurements from the sensor system to a mobile computing device,processing the measurements to track and analyze any change in thecircumference, orientation, relative position, etc. of the body portion,and generating an alert output based, at least in part, on the analyzedchange in circumference, orientation, relative position, etc. In someembodiments, processing the measurements (e.g., circumferential,relative positon, position over time, etc.) to track and analyze anychange in movement, activity, circumference, etc. is performed fully orpartially by the mobile computing device. In some embodiments,processing the measurements (e.g., circumferential, relative positon,position over time, etc.) to track and analyze any change is performedfully or partially by a network computing device that may receive themeasurements (e.g., circumferential, relative positon, position overtime, etc.) from the mobile computing device. In some embodiments, themethod further includes querying the individual for user inputs. In suchembodiments, the alert output may also be based, in part, on these userinputs. Additionally or alternatively, in some embodiments, the methodalso includes transmitting the measurements, user inputs, and/or otherdata acquired by the mobile computing device to a healthcare provider,coach, or other authorized user.

Another aspect of the disclosure is directed to a monitoring systemconfigured to detect changes (e.g., circumferential, orientation,relative positon, position over time, etc.) to a portion of a body. Themonitoring system includes a sensor system wearable on or around aportion of an individual's body, which is configured to obtain andtransmit a plurality of measurements (e.g., circumferential, relativepositon, orientation, position over time, etc.) of the body portion overa period of time. The monitoring system also includes a mobile computingdevice, which includes a processor and a non-transitorycomputer-readable medium with instructions stored thereon. Theinstructions, when executed by the processor, cause the processor to:receive the transmitted measurements (e.g., circumferential,orientation, relative positon, position over time, etc.), process themeasurements to track and analyze any change in the body portion, andgenerate an alert output based, at least in part, on the analyzedchange. In some embodiments, the instructions stored on thecomputer-readable medium further cause the processor to query theindividual for user inputs. In such embodiments, the alert output mayalso be based, in part, on these user inputs.

In some embodiments, the monitoring system is configured to monitor forabnormal swelling of a limb, for example, swelling caused byinterstitial edema, deep vein thrombosis, pulmonary embolism,lymphedema, or other medical condition. In such embodiments, themonitored body portion may be, for example, one or both legs. The bodyportion of some embodiments includes the right and left legs or arms ofan individual, and the sensor system includes a first componentconfigured to obtain a first plurality of measurements (e.g.,circumferential, position, orientation, etc.) over time from a fixedlocation on the right leg or arm, and a second component configured toobtain a second plurality of measurements over time from an equivalentfixed location on the left leg or arm. In some such embodiments,processing the measurements to track and analyze any change (e.g.,circumferential, relative positon, position over time, orientation,etc.) includes: comparing the first plurality of measurements (e.g.,circumferential, relative positon, orientation, position over time,etc.) to each other to detect a change in right leg or arm over time,comparing the second plurality of measurements (e.g., circumferential,relative positon, orientation, position over time, etc.) to each otherto detect a change in the left leg or arm over time, and calculating adifference between the change of the right leg or arm and the change ofthe left leg or arm. The difference between the change of the right legor arm (e.g., circumferential, relative positon, position over time,orientation, etc.) and the change of the left leg or arm (e.g.,circumferential, relative positon, position over time, orientation,etc.) may contribute to a determination of a timing or content of thealert output. For example, the alert output may be generated when thedifference between the change in the right leg or arm and the change inthe left leg or arm exceeds a threshold value.

In some embodiments of the monitoring system, the user inputs promptedand received by the mobile computing device include symptoms and/or riskfactor data. Additionally or alternatively, the user inputs may includean indication of whether the individual has complied with a prescribedinstruction. The prescribed instruction may be prescribed by ahealthcare provider or the monitoring system. In some embodiments, theprescribed instructions are customizable by a healthcare provider via aremote computing device communicatively coupled to the mobile computingdevice.

The mobile computing device may be further configured to compute acompliance score indicative of the degree to which the individualcomplied with the prescribed instructions. The compliance score may becalculated based on one or more of: the change in a body portion (e.g.,circumferential, relative positon, position over time, orientation,etc.), the user inputs, detected motion of the body portion indicativeof an exercise, and a detected orientation of the body portion. Forexample, if the prescribed instructions include an instruction toupwardly tilt or elevate the legs, the compliance score may bedetermined, at least in part, by monitoring leg orientation. Such asensor system may include a gyroscope. If the prescribed instructionsinclude an instruction to perform leg exercises, the compliance scoremay be determined, at least in part, by monitoring leg movement. Such asensor system may include an accelerometer. If the prescribedinstructions include an instruction to administer a medication, thecompliance score may be determined, at least in part, from auser-entered input indicating medication administration. The compliancescore may be transmitted by the mobile computing device to a networkcomputing device in order to be accessible to a healthcare provider orother authorized user.

In some embodiments of the monitoring system, the alert output includesan instruction to the individual to consult a healthcare provider forevaluation. In some embodiments, the alert output is generated when anoverall score exceeds a predefined threshold. The overall score maycorrespond to a likelihood of onset of a disease that causes abnormalswelling of a limb. For example, the overall score may correspond to thelikelihood that the individual has developed interstitial edema, deepvein thrombosis, pulmonary embolism, or lymphedema. Various parametersmay contribute to the overall score, including one or more of: thechange in a body portion (e.g., circumferential, relative positon,position over time, orientation, etc.), a skin temperature at the bodyportion, a skin color at the body portion, one or more user inputsrelated to symptoms or risk factors, and the compliance score.

In some embodiments, the monitoring system can comprise mechanisms forbrokering or supporting communication between multiple parties,including multiple healthcare providers, family, friends, home careproviders, etc.

In some embodiments, the monitoring system is configured to monitor forchanges in the circumference of a body portion resulting from weightgain, weight loss, the development of a fetus within a woman's uterus,or changes in muscle mass. In such embodiments, the body portion mayinclude one or more of a limb (or limbs), an upper torso (i.e., chest),and a lower torso (i.e., waist). The user inputs prompted and receivedby the mobile computing device may include data inputs related to one ormore of: an exercise performed, a food consumed, a supplement consumed,a medication administered, duration of sleep, and a user-perceivedwellness rating. The alert output may include an evaluation of weightloss progress, fetal development, or strength training effectiveness orprogress. The mobile computing device of the monitoring system may befurther configured to output guidance, such as recommended exercises,meal plans, and/or other wellness tips and reminders tailored to theindividual based on one or more of: the change in the body portion(e.g., circumferential, relative positon, orientation, position overtime, etc.), detected movement of the body portion, and the user inputs.

In various embodiments of the monitoring system, the sensor systemincludes a stretchable component and a sensor module coupled thereto.The stretchable component is configured to fit securely around the bodyportion. The stretchable component may be formed of a stretchable band,sleeve, belt, brace, or garment such as a sock, legging, or shirt. Insome embodiments, the sensor module includes: an electrical componentconfigured to undergo a change when the stretchable component isstretched, and a sensor configured to detect the change. The change mayinclude a change in a parameter such as inductance, resistance, orcapacitance. In such embodiments, the changed parameter correlates to,and is indicative of, a change in circumference. In some embodiments,the sensor module includes a strain gauge configured to detect a tensileforce exerted on the stretchable component, the force being correlatedto, and indicative of, a circumference measurement.

In some embodiments, the sensor system is further configured to detectone or more of: a surface skin temperature, an orientation of the bodyportion, an acceleration of the body portion, and a color of a surfaceof the body portion. Such a sensor system may include one or more of: atemperature sensor, a gyroscope, an accelerometer, and an image sensor.

In some embodiments, the monitoring system also includes a networkcomputing device communicatively coupled to the mobile computing deviceand configured to receive and store the measurements (e.g.,circumferential, relative positon, position over time, orientation,etc.) and other data received from the mobile computing device, generateand transmit alerts to a healthcare provider or other authorized user,and store and transmit instructions and information to the mobilecomputing device. In some embodiments, the monitoring system alsoincludes a supervisor computing device communicatively coupled to thenetwork computing device. In some such embodiments, at least some of theinstructions and information transmitted from the network computingdevice to the mobile computing device are customizable by a healthcareprovider, coach, or other health or wellness professional via thesupervisor computing device.

Another aspect of the present disclosure is directed to a monitoringsystem for detecting changes to a portion of a body (e.g.,circumferential, relative positon, position over time, orientation,etc.). In some embodiments, the monitoring system includes: a sensorsystem wearable around a portion of an individual's body and configuredto obtain measurements for a plurality of parameters of the body portionover a period of time, the plurality of parameters including acircumference of the body portion and one or more of a surface skintemperature, an orientation of the body portion, a position of the bodyportion, an acceleration of the body portion, and a color of a surfaceof the body portion; a processor communicatively coupled to the sensorsystem; and a non-transitory computer-readable medium with instructionsstored thereon. The instructions, when executed by the processor, causethe processor to perform a method including: receiving the measurementsfor the plurality of parameters, applying relative weights to theplurality of parameters to generate weighted measurements, receivinginputs specifying which weighted measurements and one or more additionalfactors to include in an overall score, the one or more additionalfactors including one or more of: a compliance score and a user inputentered by the individual, calculating the overall score based on theweighted measurements and the one or more additional factors, andgenerating an alert output when the overall score exceeds a predefinedthreshold.

In some embodiments, the one or more additional factors further includea range of motion of the body portion. In some embodiments, calculatingthe range of motion of the body portion includes comparing a firstorientation of the body portion to a second orientation of the bodyportion. In other embodiments, calculating the range of motion of thebody portion includes comparing an orientation of the body portion to afirst orientation of a first body portion. In some embodiments, therange of motion of the body portion is benchmarked to a previous rangeof motion reading. In some embodiments, the range of motion of the bodyportion is compared to a future range of motion goal or a time-basedgoal. In some embodiments, the future range of motion goal is based onone or more of: an exercise, one or more user-initiated range of motionmeasurements, and time.

In some embodiments, the method further includes generating a progressindication for the range of motion of the body portion relative to thefuture range of motion goal.

In some embodiments, the compliance score includes, at least in part, acalculation of a number of repetitions performed of an exercise comparedto a prescribed or target number of repetitions. In some embodiments,the calculation of the number of repetitions is based on one or more of:a detected body portion orientation and a detected body portionmovement.

In some embodiments, the compliance score includes, at least in part, acalculation of a quality of performance of an exercise. In someembodiments, the calculation of the quality of performance of theexercise is based on one or more of: a detected body portionorientation, a detected body portion movement, a detected body portioncircumferential change, and one or more parameters derived from one ormore of: the detected body portion orientation, the detected bodyportion movement, and the detected body portion circumferential change.

In some embodiments, the calculation of the quality of performance iscompared to one or more ideal, maximum, or threshold values for the oneor more of: the detected body portion orientation, the detected bodyportion movement, the detected body portion circumferential change, andthe one or more parameters.

In some embodiments, the method further includes comparing themeasurements for the plurality of parameters to a set of previousmeasurements for the plurality of parameters.

In some embodiments, the method further includes determining, using thecomparison, a range of motion of the body portion. In some embodiments,the method performed further includes determining, using the comparison,a progress of the individual towards a time-based goal, future goal, ortarget goal for a range of motion of the body portion.

In some embodiments, the method further includes automaticallydetermining a sensor system orientation following placement of thesensor system on the body portion. In some such embodiments, the methodincludes: positioning the sensor system around the body portion;recommending that the individual perform at least one of: a recommendedmovement of the body portion and a recommended orientation of the bodyportion; receiving the measurements of the plurality of parametersduring the at least one of the recommended movement and the recommendedorientation; and calculating a sensor system orientation based on themeasurements of the plurality of parameters.

Another aspect of the present disclosure is directed to a monitoringsystem for detecting circumferential changes of a portion of a body. Insome embodiments, the monitoring system includes: a sensor systemwearable on or around a portion of an individual's body and configuredto obtain measurements for a plurality of parameters of the body portionover a period of time, the plurality of parameters including one or moreof: a circumference of the body portion, a surface skin temperature, anorientation of the body portion, an acceleration of the body portion, aposition of the body portion, and a color of a surface of the bodyportion; a processor communicatively coupled to the sensor system; and anon-transitory computer-readable medium with instructions storedthereon. The instructions, when executed by the processor, cause theprocessor to perform a method including: receiving the measurements forthe plurality of parameters, applying relative weights to the pluralityof parameters to generate weighted measurements, calculating an overallscore based on the weighted measurements and one or more additionalfactors, generating an alert output when the overall score exceeds apredefined threshold, and transmitting a notification to the individual,such that the notification provides instructions or feedback forimproving the overall score.

In some embodiments, the one or more additional factors include one ormore of: a compliance score and a user input entered by the individual.

In some embodiments, notifications or feedback are personalized for theindividual, such that personalization of the tone is based on one ormore of: a demographic, a medical history, an emotional state, aprogress, a location, a profile, and the overall score of theindividual. In some embodiments, notifications or feedback arecustomized to a user or patient profile.

In some embodiments, the monitoring system further includes a mobilecomputing device comprising the processor.

In some embodiments, the feedback includes displaying on a display ofthe mobile computing device a compliance rating of the individualrelative to one or more peers, such that the compliance rating is basedon a comparison of the overall score to an expected overall score forthe individual. In some embodiments, the feedback includes positive orencouraging messages from one or more of: a caregiver, a healthcareprovider, a family member, a friend, or a peer. In some embodiments, thefeedback includes a promised monetary or simulated award for improvingthe overall score. In some embodiments, the feedback includeseducational information about one or more long-term effects of theoverall score.

In some embodiments, the method further includes transmitting a secondnotification to one or more of: a caregiver, a healthcare provider, afamily member, a friend, or a peer, such that the second notificationincludes a compliance rating of the individual.

An additional aspect of the disclosure is directed to a leg or armmonitoring device. The leg or arm monitoring device of variousembodiments includes: a stretchable component configured to be attachedto or fit securely around a circumference of a patient's calf or arm,and a sensor module coupled to the stretchable component. The sensormodule includes various electrical components, for example, a battery, afirst sensor configured to sense a first parameter indicative of thecircumference, rotation, physical performance of the patient's calf orarm during an exercise, a processing unit configured to process thefirst parameter and detect a measurement from the first parameter, amemory storage configured to store the measurement, and an antennaconfigured to wirelessly transmit the measurement to a paired mobilecomputing device.

In some embodiments, the first parameter is selected from a groupconsisting of: inductance, resistance, capacitance, and strain. In someembodiments, the stretchable component includes a stretchable band,sleeve, belt, brace, or garment. In some embodiments, at least a portionof the sensor module is reversibly coupled to the stretchable component.In some embodiments, the stretchable component is similar to an adhesiveas on an adhesive strip or band-aide. In some embodiments, the leg orarm monitoring device is configured to detect swelling of the patient'scalf or arm consistent with the performance of a strengthening exercise,onset of interstitial edema, deep vein thrombosis, pulmonary embolism,or lymphedema. In some embodiments, the sensor module additionally oralternatively includes a sensor configured to sense a second parameterindicative of motion of the patient's calf or arm. The sensor may be anaccelerometer. Additionally or alternatively, the sensor module mayinclude an additional sensor configured to sense an additional parameterindicative of an orientation of the patient's calf or arm. Theadditional sensor may be a gyroscope. In some embodiments, the sensormodule additionally or alternatively includes one or more of atemperature sensor and an image sensor. In some embodiments, the sensormodule is configured to provide a measurement of tightness of thestretchable component. In some embodiments, the leg or arm monitoringdevice or a mobile computing device communicatively coupled thereto isconfigured to generate an alert when the sensor module detects that thestretchable component is too tight. In some embodiments, the leg or armmonitoring device generates a haptic alert. In some embodiments, amobile computing device communicatively coupled to the leg or armmonitoring device is configured to generate outputs that includehealth-related feedback and/or recommendations based on one or more ofthe sensor readings.

Another aspect of the present disclosure is directed to a monitoringsystem for detecting improvement to strength and range of motion for aportion of a body. In some embodiments, the monitoring system includes:a sensor system wearable on or around a portion of an individual's bodyand configured to obtain measurements for a plurality of parameters ofthe body portion over a period of time, the plurality of parametersincluding a performance of body portion in one or more exercisesprovided as part of an adaptive care plan and one or more of a surfaceskin temperature, an orientation of the body portion, an acceleration ofthe body portion, and a color of a surface of the body portion; aprocessor communicatively coupled to the sensor system; and anon-transitory computer-readable medium with instructions storedthereon. In some embodiments, the instructions, when executed by theprocessor, cause the processor to perform a method comprising: receivingthe measurements for the plurality of parameters, extracting a patternfrom the measurements, wherein the pattern comprises a range of motionof the body portion, comparing the pattern to a template or baselinepattern, determining a change in the range of motion of the body portionbased on the comparison of the pattern with the baseline pattern,receiving one or more user inputs specifying patient reported inputs,comparing the one or more user inputs to one or more baseline userinputs, determining a change in the one or more user inputs relative tothe one or more baseline user inputs, calculating an overall score basedon the change in the range of motion and the change in the one or moreuser inputs, and generating an alert output when the overall scoreexceeds a predefined threshold.

In some embodiments, the method performed by the processor furthercomprises measuring one or more planar movements of the body portion todetermine the baseline pattern. In some embodiments, the methodperformed by the processor further comprises determining the baselinepattern using pre-operative planar movements of the body portion. Insome embodiments, the method performed by the processor furthercomprises determining the baseline pattern using pre-operative planarmovements of a plurality of body portions of a plurality of individuals.The pre-operative planar movements may be normalized or averaged acrossthe plurality of individuals.

In some embodiments, the method performed by the processor furthercomprises determining a number of repetitions completed by the bodyportion. In some embodiments, the method performed by the processorfurther comprises comparing the number of repetitions to a prescribednumber of repetitions to determine whether the prescribed number ofrepetitions was achieved. In some embodiments, the method performed bythe processor further comprises calculating the overall score based onthe change in the range of motion, the change in the one or more userinputs, and the number of repetitions.

In some embodiments, the patient reported inputs comprise one or moreof: symptoms, pain level, subjective statements on mobility, medicationadherence, emotional state, attitude towards recovery, a duration ofsleep attained, a food consumed, a daily wellness rating, a supplementconsumed, risk factor data, and any combination thereof. In someembodiments, the patient reported inputs comprise patient self-reportsincluding one or more of: pain level, daily activities, symptoms, andsubjective statements on mobility.

In some embodiments, the plurality of parameters comprise patientgenerated health data, and the monitoring system is configured todynamically adjust a prediction and generate a simulation that is usedto improve patient adherence to an adaptive care plan.

In some embodiments, the overall score comprises an overall adherenceand recovery score based on patient generated heath data or the patientreported inputs, and wherein the patient generated health data comprisesone or more of: the range of motion, reduction in pain, improvement ingait, strength improvement, stability improvement, and a combinationthereof.

In some embodiments, the plurality of parameters further comprises ameasure of quality of performance of the one or more exercises, andwherein feedback regarding the quality of performance is assesseddynamically and provided to the patient in real-time. In someembodiments, the quality of performance of the one or more exercisescomprises one or more of: a measure of flexibility, a measure ofstrength, a measure of endurance, a measure of timing, a measure ofsmoothness of movement, a measure of shakiness of movement, positionalinformation, relative fatigue levels, a measure of speed of movement,and a combination thereof.

In some embodiments, the alert output is generated depending on one ormore inputs provided by the user, wherein the processor assesses theprobability of the user adhering to a care protocol and simulatesmultiple alert outputs, such that a generated alert output is providedto the user based on the one or more simulated outputs.

In some embodiments, the instructions stored on the computer-readablemedium further cause the processor to query the individual for the oneor more user inputs. In some embodiments, the one or more user inputscomprise an indication of whether the individual has complied with aprescribed instruction.

In some embodiments, the sensor system comprises an adhesive componentand a sensor module coupled thereto, wherein the adhesive component isconfigured for use for a predefined fixed interval before replacement.

In some embodiments, the body portion comprises a limb, upper torso, orlower torso. In some embodiments, the body portion comprises a right legand a left leg of the individual, and wherein the sensor systemcomprises: a first component configured to obtain a plurality ofpositional and orientational measurements over time from a fixedlocation on the right leg, and a second component configured to provideauditory feedback to the patient in real-time, based on performance ofthe user as measured by the sensory system.

In some embodiments, processing the positional and orientationalmeasurements to identify and analyze a change comprises one or more of:comparing the plurality of positional and orientational measurements todetect a change in the adherence of a user to an adaptive care plan orthe probability of complete recovery by a particular time.

In some embodiments, an overall score is calculated by the processorbased at least in part on measurement of the body portion and one ormore user-entered inputs related to symptoms or risk factors. In someembodiments, the overall score is further based on a measure ofcompliance with prescribed instructions, the measure of compliancedetermined from one or more of: a detected body portion orientation, adetected body portion movement, and a user-entered input related tocompliance.

In some embodiments, the one or more exercises comprise flexion,extension, abduction, adduction, or a combination thereof.

In some embodiments, the method performed by the processor furthercomprises transmitting a notification to the individual, wherein thenotification provides instructions or feedback for improving the overallscore. In some embodiments, a tone of the notification or feedback ispersonalized for the individual, wherein personalization of the tone isbased on one or more of: a demographic, number of sessions completed,number of sessions missed, goals achieved, previous response tomotivation messages and notifications, pain level, a medical history, anemotional state, calculated probability of attending session next day,simulated scenario of actions taken by the patient, a progress, alocation, a profile, and the overall score of the individual. In someembodiments, the feedback comprises displaying on a display of themobile computing device a compliance rating of the individual relativeto one or more peers, wherein the compliance rating is based on acomparison of the overall score to an expected overall score for theindividual. In some embodiments, the feedback comprises positive orencouraging messages from one or more of: a caregiver, a healthcareprovider, a family member, a friend, or a peer.

In some embodiments, the method performed by the processor furthercomprises transmitting a second notification to one or more of: acaregiver, a healthcare provider, a family member, a friend, or a peer,wherein the second notification includes a compliance rating of theindividual. In some embodiments, the feedback comprises a promisedmonetary or simulated award for improving the overall score. In someembodiments, the feedback comprises educational information about one ormore long-term effects of the overall score.

Another aspect of the present disclosure is directed to a leg monitoringdevice. In some embodiments, the device comprises a component configuredto attach securely to a patient's leg or arm; and a sensor modulecoupled to the component, wherein the sensor module comprises: abattery, a first sensor configured to sense a first parameter indicativeof the position and orientation of the patient's leg or arm, and whereinthe sensor module comprises: a first component configured to obtain aplurality of positional and orientational measurements over time fromthe position and orientation of the patient's leg or arm, and avoice-enabled component configured to provide auditory feedback to thepatient in real-time, based on performance of the user as measured bythe sensory module, a processing unit configured to process the firstparameter and detect a position and orientation measurement from thefirst parameter, a memory storage configured to store the position andorientation measurements, and an antenna configured to wirelesslytransmit the position and orientation measurements to a paired mobilecomputing device.

In some embodiments, the first parameter is selected from a groupconsisting of: inductance, resistance, magnetism, and capacitance. Insome embodiments, the component comprises a stretchable band, sleeve,belt, brace, or garment. In some embodiments, at least a portion of thesensor module is reversibly coupled to the component.

In some embodiments, the leg monitoring device is configured to assessesthe probability of the user adhering to a care protocol and simulatesmultiple alert outputs, such that a generated alert output is providedto the user based on the one or more simulated outputs.

In some embodiments, the sensor module further comprises a second sensorconfigured to sense a second parameter indicative of motion of thepatient's leg or arm.

In some embodiments, the second sensor is an accelerometer ormagnetometer. In some embodiments, the sensor module further comprises athird sensor configured to sense a third parameter indicative of acircumference of the patient's leg or arm. In some embodiments, thethird sensor is a gyroscope or magnetometer. In some embodiments, thesensor module further comprises one or more of a temperature sensor andan image sensor. In some embodiments, the sensor module is configured toprovide a measurement of tightness of the component.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing is a summary, and thus, necessarily limited in detail. Theabove-mentioned aspects, as well as other aspects, features, andadvantages of the present technology will now be described in connectionwith various embodiments, with reference made to the followingaccompanying drawings:

FIG. 1A illustrates an exemplary overview of interactive components ofthe monitoring system.

FIG. 1B illustrates the elements and/or adaptive aspects of the patientnotification system.

FIG. 1C illustrates a schematic block diagram of one embodiment of asystem for monitoring health parameters of an individual, includingcircumferential changes to a portion of a body.

FIG. 2 illustrates a flow chart of one embodiment of a method of usingthe monitoring system of FIG. 1C.

FIG. 3 illustrates a functional block diagram of one embodiment of asensor system provided within the monitoring system of FIG. 1C.

FIGS. 4A-4L schematically illustrate a plurality of examples of thesensor system of FIG. 3 .

FIGS. 4M-4O illustrate exemplary measurements collected from sensorsmonitoring joint extension and/or limb movement.

FIG. 5 illustrates a flow chart of one embodiment of a method performedby the sensor system of FIG. 3 .

FIG. 6 illustrates a functional block diagram of one embodiment of amobile computing device provided within the monitoring system of FIG.1C.

FIGS. 7A-7K schematically illustrate a plurality of examples ofgraphical user interfaces displayed by the mobile computing device ofFIG. 6 .

FIG. 8 illustrates a flow chart of one embodiment of a method performedby the mobile computing device of FIG. 6 .

FIG. 9 illustrates a flow chart of another embodiment of a methodperformed by the mobile computing device of FIG. 6 .

FIG. 10 illustrates a flow chart of one embodiment of a method forproviding instructions or feedback for improving an overall score.

FIG. 11 illustrates a flow chart of one embodiment of a method fortransmitting a notification about a compliance rating of an individual.

FIG. 12 illustrates a flow chart of one embodiment of a method forcalculating a sensor system orientation.

FIG. 13A-13C illustrates inputs to the system and dynamic adjustment andreadjustment of predicted progress based on the inputs.

FIG. 14A-14E illustrates exemplary computational aspects of the systemincluding features of the system and methods for model analysis.

FIG. 15 illustrates a flow chart of one embodiment of a method formeasuring a number of repetitions performed of an exercise.

FIG. 16 illustrates a flow chart of another embodiment of a method formeasuring a quality of performance of an exercise.

FIG. 17 illustrates a schematic block diagram of one embodiment of anetwork computing device provided within the monitoring system of FIG.1C.

The illustrated embodiments are merely examples and are not intended tolimit the invention.

DETAILED DESCRIPTION

The following description is not intended to limit the invention tothese described embodiments, but rather to enable any person skilled inthe art to make and use this invention. Other embodiments may beutilized and modifications may be made without departing from the spiritor the scope of the subject matter presented herein. Aspects of thedisclosure, as described and illustrated herein, can be arranged,combined, and designed in a variety of different configurations, all ofwhich are explicitly contemplated and form part of this disclosure.

Throughout and within this specification, one or more publications maybe referenced to more fully describe the state of the art. Thedisclosures of each of these references are incorporated herein byreference in their entireties as though they also form part of thisdisclosure.

Unless otherwise defined, each technical or scientific term used hereinhas the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs.

As used in the description and claims, the singular form “a”, “an” and“the” include both singular and plural references unless the contextclearly dictates otherwise. For example, the term “a limb” may include,and is contemplated to include, a plurality of limbs. At times, theclaims and disclosure may include terms such as “a plurality,” “one ormore,” or “at least one;” however, the absence of such terms is notintended to mean, and should not be interpreted to mean, that aplurality is not conceived.

The term “about” or “approximately,” when used before a numericaldesignation or range (e.g., a change in force or circumference),indicates approximations which may vary, for example, by (+) or (−) 5%.All numerical ranges provided herein are inclusive of the stated startand end numbers. The term “substantially” indicates mostly (i.e.,greater than 50%) or essentially all of an element, process, component,device, or system.

As used herein, the term “comprising” or “comprises” is intended to meanthat the devices, systems, and methods include the recited elements, andmay additionally include any other elements. “Consisting essentially of”shall mean that the devices, systems, and methods include the recitedelements and exclude other elements of essential significance to thecombination for the stated purpose. Thus, a system or method consistingessentially of the elements as defined herein would not exclude othermaterials, features, or steps that do not materially affect the basicand novel characteristic(s) of the claimed invention. “Consisting of”shall mean that the devices, systems, and methods include the recitedelements and exclude anything more than a trivial or inconsequentialelement or step. Embodiments defined by each of these transitional termsare within the scope of this disclosure.

Overview

Disclosed herein are devices, systems, and methods for a providing aunified care system for adaptively improving the musculoskeletalperformance of a user (e.g., patient, athlete, etc.). A unifiedmonitoring system as disclosed herein comprises: adaptable contentprovided to the user, sensors for use by the user outside of asupervised medical facility, a care cloud comprising systems and devicesfor collecting meaningful recovery data from sensors used by the userand from information provided by the user, and communication tools thatfacilitate and broker timely communication between healthcare providers,the user's support network (e.g., family, friends, home care providers,etc.) and the user. Disclosed herein are also devices, systems, andmethods for monitoring one or more health parameters of an individual,including gait, movement (e.g., movement of body portion relative togravitational direction and position), orientation, and circumferentialchanges to one or more body portions. The devices, systems, and methodsof various embodiments are additionally intended to track and increasecompliance with health and wellness recommendations and improve healthand wellness outcomes.

FIG. 1A illustrates various data-based feedback loops that are involvedin gathering data. Data-based feedback include patient reported outcome(PRO), patient-generated health data (PGHD), and real-time feedback tothe patient and care providers and caregivers as needed to guidepatients through recovery. PRO data comprises patient self-reports abouttheir recovery including symptoms, pain level, subjective statements onmobility, etc. PGHD data comprises automatically generated data aboutthe patient from the sensors. As shown in FIG. 1A, the monitoring systemmanages three components: the sensor/device that are used by thepatient, the care cloud that collects/stores/analyzes data collectedfrom the sensor and from patient feedback, and thehealthcare/caregiver/support teams that provide empathetic guidance andsupport to the user/patient. The monitoring system does this byproviding contextual and personalized messages to engage, adhere, andprogress the patient through their care plan. The monitoring system alsoprovides real-time provider analytics and reports such that the careteam and healthcare providers may efficiently and effectively respond tothe needs of the patient. Finally, the monitoring system providespriorities and rules-based provider alerts that may help providersefficiently manage their time and user/patient care.

FIG. 1B illustrates various clinical and social aspects of healthcarethat are integrated into the monitoring system using the sensor,application, and care cloud model to improve patient outcomes and getpatients back to their daily routines. The system provides the keyfeatures of engagement, timely guidance, education, and social support.Engagement with the user/patient helps to facilitate exercise adherenceand walking, while the timely guidance supports other activities andmedical adherence. The education component provides users with insightinto methods for helping the user heal faster including methods foricing and holistic methods. Finally, the application provides socialsupport, which allows the user's symptoms to be reported to the systemand for the system to broker interactions between the user/patient andtheir care team. Collectively, these aspects help to improve the metricsthat are monitored by the system, which include the range of motion ofthe user/patient, their pain levels and their daily activities, suchthat the user/patient may easily and quickly recover efficiently andproduce good clinical outcomes.

FIG. 1C illustrates one example of a health monitoring system (e.g.,system comprising care cloud, patient profile generators, adaptivepatient user interfaces, sensors, healthcare interfaces, support teamcommunications, etc.) configured to obtain, analyze, and respond topositional, orientational, and circumferential measurements of a bodyportion of an individual. As illustrated, the monitoring system 100includes a sensor system 110, a mobile computing device 120, and anetwork computing device 130. The system 100 may additionally beconfigured to form a connected network in which physicians, coaches,and/or other authorized users can track the progress of the monitoredindividual and/or individualize instructions and feedback provided tothe monitored individual. In such embodiments, the health monitoringsystem 100 includes one or more additional computing devices, includingone or more supervisor computing devices 140, one or more reviewercomputing devices 150, and/or one or more administrator computingdevices 160.

In various embodiments, the sensor system 110 is configured to be wornby a subject. A subject who wears the sensor systems described hereinmay be interchangeably referred to as a user, patient, individual,person, or athlete. It will be appreciated by those skilled in the artthat the subject monitored by the various devices and systems describedherein may be any mammal or other animal.

The sensor system 110 may be formed of: a stretchable componentconfigured to fit securely on or around a body portion of theindividual, and a sensor module coupled thereto. The stretchablecomponent may be a strap, brace, belt, garment, adhesive bandage, orother wearable material designed to be attached to or fitted around thebody portion. As used herein, the body portion may refer to one or bothlegs, one or both arms, a torso, a chest, a belly, a waist, a head,and/or other body part. The sensor module may be configured to sense theamount of stretch experienced by the stretchable component, the quality,frequency, duration, or other characteristic of movement and/or detect acircumference measurement from a sensed stretch.

In various embodiments, the sensor system 110 may comprise one or moremagnetic components that are adhered to the body portions. In someembodiments, sensors may use a magnetic mounting system so thatlocalized body motion, orientation, circumferential width increase, andlocalized temperature variations may be detected. In some embodiments,the magnet is inside the sensor and attached to a magnetized adhesivestrip which may be placed anywhere on the body.

The sensor module may be configured to measure one or more aspects of abody portion relative to gravity. The sensor module may comprise asingle sensor, dual sensors, or greater than two sensors. The sensormodule may be configured to monitor the speed, time, and/or smoothnessof the movement of the body portion. In various configurations, thesensor module may be configured such that it may rely on magneticreadings. In further embodiments, a sensor may be configured to performmagnetic readings relative to gravity, and may detect twisting motion,vibrating motion, and changes in orientation relative to the X, Y,and/or Z planes. As used herein, the sensor module includes all sensors,power supply, signal processing electronics, controlling logic, anddigital transmission devices needed to sense movement, orientation, orstretch, obtain measurements of the body portion or body portions, andtransmit the measurements to the mobile computing device 120. The sensormodule may additionally include other sensors such as sensors configuredto detect orientation, acceleration, temperature, and/or color.

As used herein, the mobile computing device 120 refers to both thehardware and the application software of the computing device thatcommunicates with the sensor system 110. The mobile computing device 120is configured to receive, process, and analyze sensor data from thesensor system 110. It may be further configured to adaptively query anindividual for user inputs, generate reminders and other alerts to theindividual, provide access to relevant health-related information, andgenerate and transmit messages intended for physicians, coaches,caregivers, or other authorized users of the system. Queries to a usermay be customized to a user profile and/or one or more distinctcharacteristics of the patient or user including but not limited to: thenumber of sessions completed by a patient, the number of sessions missedby a patient, goals achieved by a patient, response to previous messagesprovided by monitoring system, and/or pain level.

In some embodiments, the mobile computing device 120 is a smartphone,wearable computing device, notebook computer, laptop computer, tablet,or other portable computing device configured to pair with the sensorsystem 110. In other embodiments, the mobile computing device 120 may beany other personal computing device configured for wired or wirelessconnection to the sensor system 110.

As shown in FIG. 1C, the mobile computing device 120 is connected, atleast at times, to the sensor system 110 via a communication link. Insome embodiments, the mobile computing device 120 is wirelessly coupledto the sensor system 110 via a nearfield communications (NFC) protocol,a low energy Bluetooth® protocol, or other radiofrequency (RF)communication protocol. In some embodiments, the sensor system 110 isadditionally or alternatively configured to communicate with the mobilecomputing device 120 via a databus and a wired (e.g., removable cable)connection. In some embodiments, communication between the sensor system110 and the mobile computing device 120 is bidirectional; in otherembodiments, communication is unidirectional with data pushed from thesensor system 110 to the mobile computing device 120.

In various embodiments, the mobile computing device 120 is coupled tothe network computing device 130 via a bidirectional communication link.In particular, the mobile computing device 120 may be connected to thenetwork computing device 130 via a CDMA, GSM, LTE, or other cellularnetwork, via Wi-Fi®, or via any other suitable wireless or wiredcommunication protocol. If one or more supervisor computing devices 140,reviewer computing devices 150, and/or administrator computing devices160 are present in the system, such devices are also connected to thenetwork computing device 130 via a bidirectional communication link,such as a cellular network, Wi-Fi, other wireless communicationprotocol, or via a cable internet, dial-up internet, Ethernet, or otherwired means of connection.

The network computing device 130 of some embodiments is a cloud-basedserver (e.g., care cloud). It may be formed of one or multiple computingdevices, including an application server, an internet server, a databaseserver, or a combination thereof. In some embodiments, the networkcomputing device 130 is operated, managed, controlled, maintained, orowned by a system administrator. The network computing device 130 refersto the hardware and software that contains and implements an analyticssystem. The analytics system refers to the backend system that storesall user data. It also stores all instructions that are transmitted toand downloadable by the mobile computing device 120. These includeapplication instructions (i.e., software) and prescribed health-relatedinstructions intended for the monitored individual. The analytics systemof some embodiments is also configured to perform analytics of amonitored individual's data and population-wide data. The analyticssystem may also be configured for integration with electronic medicalrecords.

In various embodiments, a cloud-based server or care cloud may comprisea cloud based content management and storage system, configured as acentral intelligence for the monitoring system. A care cloud may beconfigured with comprehensive knowledge about a user's (e.g., patient's)current state and historic state. A care cloud may collect informationand intelligence about patients across providers and, using thecollected information, may generate deductions, modification,refinements, etc. to care plans. A care cloud may correlate dataelements to track and improve care delivery and adherence for a singlepatient and for groups of patients.

In some embodiments, one or more supervisor computing devices 140 areprovided within the monitoring system. As used herein, a supervisorcomputing device 140 is any computing device used by a health orwellness professional to interact with the analytics system of thenetwork computing device 130. As used herein, a health or wellnessprofessional and/or healthcare provider is also referred to as asupervisor and is intended to include any individual who oversees anaspect of the care, health, or wellness of the monitored individual. Thesupervisor may be, for example, an athletic coach, personal trainer, orhealthcare provider. A healthcare provider, as used herein, refers to aprofessional responsible for the healthcare of the monitored individual.The healthcare provider may be a physician, physician assistant, medicaltechnologist, other medical assistant, nurse, nurse practitioner,podiatrist, chiropractor, dietician, midwife, obstetrician, or any otherhealthcare professional. In some embodiments, a supervisor is able toaccess an application-based or web-based internet portal using thesupervisor computing device 140, which enables the supervisor tointeract with the analytics system of the network computing device 130.Through the supervisor portal, the supervisor can: review data acquiredfrom the sensor system 110; configure and modify alert algorithms, whichthe monitoring system uses to determine when to generate alerts (e.g.,for the monitored individual, healthcare providers, care providers orothers) and what alerts to generate; create, customize, and/or modifyprescribed instructions for the monitored individual; select specificparameters for the sensor system 110 to monitor; select specificparameters and/or measurements for inclusion in an overall score for themonitored individual; and select or compose messages for transmission tothe mobile computing device 120 of the monitored individual and/or otherapproved users or reviewers.

Additionally or alternatively, in some embodiments, one or more reviewercomputing devices 150 are provided within the monitoring system. As usedherein, a reviewer computing device 150 is any computing device used bya reviewer to interact with the analytics system of the networkcomputing device 130. As used herein, a reviewer is any trustedindividual who has been granted access, by a monitored individual or asupervisor, to review the data of a particular monitored individual. Thereviewer may be a caregiver, friend, family member, guardian, or otherindividual concerned with the welfare of the monitored individual. Insome embodiments, a reviewer is able to access an application-based orweb-based internet portal using the reviewer computing device 150, whichenables the reviewer to interact with the analytics system of thenetwork computing device 130. In some embodiments, a supervisor orreviewer may use a healthcare provider interface to access, review, orinteract with the analytics system of the network computing device 130.Through the reviewer portal, the reviewer may be able to: review all ora limited portion of the data acquired from the sensor system 110;select or compose messages of encouragement or other feedback fortransmission to the mobile computing device 120 of the monitoredindividual; and/or generate and send questions to a healthcare provideror other supervisor.

The network computing device 130 may include one or more input devices,output devices, and/or communicatively coupled administrator computingdevices 160 through which a system administrator can create and maintainthe analytics system.

Each of the supervisor computing devices 140, reviewer computing devices150, and administrator computing devices 160 may be any suitablecomputing device, including, for example, a smartphone, wearablecomputing device, notebook computer, laptop computer, tablet, or desktopcomputer.

Together, the components of the monitoring system 100 function toexecute various algorithms and perform various methods, includingobtaining, analyzing, and responding to measurements (e.g.,circumferential, positional, relational, orientational, smoothness,etc.) of a body portion.

FIG. 2 depicts one example of a method 200 of using the monitoringsystem 100 described above. As shown at block 210, the method includespositioning the sensor system 110 on or around a body portion. Thesensor system 110 may be secured on or around the body portion by themonitored individual or with the help of a physician, athletic trainer,other supervisor, friend, family, caregiver, or other reviewer. Thesensor system 110 of some embodiments may be reusable and configured topermit repeated attachment to and detachment from the body portion. Insome embodiments, the sensor system 110 is shaped to conform to one ormore contours of the individual's body or is otherwise configured so asto facilitate accurate positioning of the sensor system 110 at the samelocation each time it is worn. In some embodiments, the sensor systemmay be disposable. A sensor system may be adhered to the body portion byan adhesive. A sensor system may be configured to stretch or adapt tothe movement or contours of the body portion during performance of anactivity.

As shown at block 220, the method 200 further includes obtaining aplurality of measurements of the body portion via the sensor system 110,including, for example, a plurality of positional, orientational, and/orcircumferential measurements. As described in more detail in the nextsection, in some embodiments, obtaining the plurality of measurementsincludes: obtaining a baseline, sensing a change in a parameterindicative of and correlated to a change in the measurement (e.g.,circumferential, positional, relational, orientational, smoothness,etc.), and calculating a measurement from the sensed change in theparameter. The calculated measurement (e.g., circumferential,positional, relational, orientational, smoothness, etc.) may be arelative measurement (i.e., a measure of change from the baseline orfrom a previous measurement) or an absolute measurement. In someembodiments, obtaining a plurality of measurements (e.g.,circumferential, positional, relational, orientational, smoothness,etc.) of the body portion further includes obtaining measurements (e.g.,absolute or relative measurements) of one or more additionalhealth-related parameters. For example, in some embodiments, the sensorsystem 110 is configured to obtain measurements indicative of one ormore of a change in: orientation, movement (i.e., acceleration), color,and temperature of the body portion. Additionally or alternatively, insome embodiments, the sensor system 110 is configured to obtainmeasurements indicative of pulse, heart rate, blood oxygenation (i.e.,pulse oximetry), blood volume (i.e., plethysmography), range of motionas described elsewhere herein, and/or other health parameters.

The method 200 also involves transmitting the measurements from thesensor system 110 to a communicatively coupled mobile computing device120 or other computing device, as shown at block 230. The transmittedmeasurements may include any obtained by the sensor system 110,including, for example, circumference, orientation, acceleration, color,and/or temperature.

At block 240, the measurements are processed to track and analyzechanges in the body portion. In some embodiments, measurements (e.g.,circumferential, positional, relational, orientational, smoothness,etc.) are tracked over time and changes are analyzed, for example, todetermine when the change (e.g., circumferential, positional,relational, orientational, smoothness, etc.) has exceeded a predefinedthreshold value. Similarly, any other parameters being measured may betracked over time and analyzed, for example orientation or accelerationis tracked over time to determine a range of motion of the body portion.In some embodiments, each measured parameter contributes to an overallrisk score or wellness score, and analyzing the measurements involvesweighting the changes in each parameter, calculating an overall score,and determining if the score has exceeded a predefined threshold value.In some embodiments, processing the measurements to track and analyzechanges is performed partially or fully by the mobile computing device120. Additionally or alternatively, in some embodiments, some of or allthe processing, tracking, and analysis is performed on the sensor system110. Additionally or alternatively, in some embodiments, some of or allthe processing, tracking, and analysis is performed by a networkcomputing device 130.

Optionally, in some embodiments, the method 200 further includesquerying the individual for user inputs, as shown at block 250. Suchqueries are presented to a monitored individual on the mobile computingdevice 120. The requested user inputs may vary depending on the intendeduse of the monitoring system 100. For example, the mobile computingdevice 120 may prompt a user to enter one or more of: current painlevel, emotional state, attitude towards recovery, time with family orfriends, daily activities (e.g., chores, television watching, reading,etc.), biographical information; the user's current weight, age, and/orheight; medical history; family medical history; current symptoms; riskfactor data; a pregnancy status (e.g., a gestation age, conception date,pre-mature birth risk factors, or due date); an exercise performed; afood consumed; a supplement consumed; a medication administered; aduration of sleep attained; a daily wellness rating; and an indicationof whether the monitored individual has complied with a prescribedinstruction or exercise.

The monitoring system 100 generates an alert output at block 260. Thealert output may be a visual, tactile, and/or audio output generated bythe mobile computing device 120. The alert output may provide a warning,recommendation, positive feedback, progress alert, or any other usefulmessage. The alert output is based, at least in part, on the analyzedchange in position, orientation, and/or circumference. Alternatively,the alert output is based, at least in part, on the analyzed change inone or more measured parameters or a change in range of motion of thebody portion. For example, the alert output may be generated by themobile computing device 120 upon detecting that the change (e.g.,circumferential, positional, relational, orientational, smoothness,etc.) exceeded a predefined threshold. In other embodiments, the alertoutput is generated by the mobile computing device 120 at a regular timeinterval, and the information conveyed in the alert output variesdepending on the body portion's change (e.g., positional, shaking,circumferential, orientational, range of motion, extension, etc.). Insome embodiments, the alert output may also be based, in part, on theanalysis of other parameters being measured and/or the user inputs. Insome embodiments, alert outputs may also be transmitted to one or moresupervisor and/or reviewer computing devices to alert a supervisor orreviewer of important changes, progress, or status of the monitoredindividual.

FIGS. 10 and 11 depict another example of a method 1000 and 1100,respectively, of using the monitoring system 100 described above. Asshown at blocks 1010/1110 and 1020/1120, the method includes positioninga sensor system on or around a body portion and receiving measurementsfor a plurality of parameters, as described elsewhere herein. At blocks1030/1130, the method includes calculating an overall score based on themeasurements and one or more additional factors.

The overall score may be an overall risk score or wellness score. Insome embodiments, the overall score corresponds to a likelihood of onsetof a disease that causes abnormal swelling of a limb. For example, theoverall score may correspond to the likelihood that the monitoredindividual will recover from an operation or procedure and regainstrength, coordination, balance, range of motion, or other metric ofimprovement. In some instances, the overall score may indicate thesuccessful completion and physical rehabilitation of a patient by acertain date or within a certain time period or range.

As another example, the overall score may correspond to a monitoredindividual's level of success in improving overall wellness and/oradherence to a prescribed care plan. Such a score may be applicable, forexample, when the monitoring system is being used to track gradualchanges (e.g., circumferential, positional, orientational, relational,smoothness, etc.) of a body portion, for example those associated withimproved musculoskeletal performance, range of motion, weight gain,weight loss, a growing fetus, or an increase in muscle mass. Such asystem may be used, for example, by individuals who are overweight,underweight, being treated for cancer, pregnant, or athletes. One, some,or all the measured parameters may contribute to the overall score,including one or more of: the change in circumference of the bodyportion, a skin temperature at the body portion, a skin color at thebody portion, and quality or range of movement of the body portion, painlevel, medication and physical therapy adherence.

In some embodiments, the overall score may comprise an overall adherenceand recovery score, indicating the likelihood that a patient willsuccessfully recover to an acceptable standard. An acceptable standardmay be defined based on range of motion, reduction in pain, improvementin gait, strength/stability improvement, or other factors. An acceptablestandard may be based on a prescribed care plan and a baselineperformance, for example, prior to a medical procedure. An overalladherence score may be generated based on one or more of the following:the quality of the performance of a prescribed exercise, improvements ofrange of motion, changes in pain level, adherence to medications,adherence to prescribed exercises, etc. Quality of performance ofexercises can comprise one or more of: the flexibility, strength, and/orendurance of an exercise, for example, joint extension measured by theone or more sensors. The one or more sensors may be configured to trackthe timing, smoothness, shakiness, positional information, relativefatigue levels—for example measurements taken over a set of repetitions,performance across days, and any other measures disclosed herein.

In some embodiments, calculating an overall score includes assigning avalue to each measurement of each parameter and summing the values tocreate an overall score. For example, a bicep with a circumferencebetween 0-5 inches may be assigned a value of 1, between 6-10 inches avalue of 2, 11-15 inches a value of 3, and 16-20 inches a value of 4 anda skin temperature between 96-99° F. may be assigned a value of 1 and100-103° F. may be assigned a value of 2. An individual having anoverall score of 2 or 3 may be deemed as “normal” or healthy whereas anindividual with a score of 4 or 5 may be deemed as unhealthy orrequiring medical attention. In other embodiments, an overall score iscalculated by assigning a relative weight to one or more measuredparameters of importance. In further embodiments, weights used tocalculate an overall score can be adapted over time based on PRO data,reported by the patient when the patient enters a response to a questionor survey or log; PGHD data, generated automatically by sensors; dataprovided by a supervisor or healthcare provider; and/or input receivedfrom the patients support network or home care providers. In someembodiments, a processor of the mobile computing device calculates anoverall score; in other embodiments, a sensor module, a supervisorcomputing device, a healthcare provider computing device or system, areviewer computing device, or a network computing device calculates theoverall score.

For example, arithmetic average of a sliding window of movement datastreaming from the sensor can be done on the sensor itself. Further,similarly multiplying precomputed weights to streaming data can be doneon the sensor, but calculating the weights itself requires moreprocessing power. Algebraic computation, such as Fourier transform, mayneed at least the mobile computing device. Heavier computations such asusing machine learning on the same on data may be performed using theprocessing power of the server.

At blocks 1040/1140, the method includes generating an alert output whenthe overall score exceeds a predefined threshold, as described elsewhereherein. In some embodiments, the predefined threshold is a personalizedthreshold based on historical tracking of the user. For example, theinitial measurements of joint extension of an individual may indicate abaseline range of motion of 60% of ideal range of motion withsignificant shaking. As such, in some embodiments, the system outputs analert when the overall score indicates that the joint extension hasdropped below the baseline (e.g., indicative of regress or degeneration)or surpassed the baseline (e.g., indicative of progress and recovery).In other embodiments, the predefined threshold is based on populationmetrics or collected population data. Such population metrics may beskewed towards a demographic of the individual or patient. In stillother embodiments, the predefined threshold is based on known healthy orunhealthy ranges for each of the parameters, for example based onempirical evidence.

At block 1050, the method includes transmitting a notification to theindividual or a caregiver, healthcare provider, family member, friend,and/or peer of the individual. A notification may be transmitted togroups of individuals, for example a care team selected by and curatedby the user. The notification provides instructions or feedback forimproving the overall score of the individual or achieving apre-determined or target overall score. In some embodiments, thefeedback includes positive or encouraging messages from a caregiver,healthcare provider, family member, friend, and/or peer. Alternativelyor additionally, the feedback includes a promised monetary or materialaward or a simulated award for improving the overall score or achievinga pre-determined or target overall score. For example, a promisedmonetary award is a gift card, a gift certificate, cash, other award, orpoints or cash-back for use in a marketplace. The marketplace may besponsored by a healthcare provider, an insurance provider, a third-partyservice, or a seller or distributor of the monitoring system.Non-limiting examples of material awards include items selected by theindividual from a marketplace, consumer products, music-relatedproducts, kitchen-related products, exercise-related products,health-related products, work-related products, leisure-relatedproducts, or any other material asset, item, or product. Non-limitingexamples of simulated awards include: lives for a cartoon or simulatedcharacter; coins, treats, cash, turns, or other incentives for use in asimulated world or game; acceleration or fast-forwarding to a moreadvanced level of a game; an increased overall ranking in a simulatedworld or game; or any other virtual or simulated award.

In some embodiments, the feedback includes educational information aboutone or more long-term effects of the overall score. For example, thesystem may compile and analyze a series of observations or measurementsfrom the individual and project a percent or other value indicative ofrange of motion (e.g., passive, active, active-assisted) that theindividual can expect to lose or secondary health conditions theindividual may acquire if the individual continues down this path.Various parameters that the system analyzes include, but are not limitedto: exercise frequency, adherence to a dosing regimen for one or moreprescriptions, adherence to prescribed exercises (e.g., quality andquantity), measured parameters from one or more sensors, adherence to arecommended diet, adherence to a visitation schedule with a healthcareprovider, and/or any additional parameters.

The instructions may include messages from a caregiver or a healthcareprovider about athletic exercises, eating parameters, dieting regimens,sleeping regimens, pharmaceutical prescriptions or dosings, or otherwellbeing activities or exercises that the user can do to improvehis/her overall score.

In some embodiments, a tone of the notification or feedback ispersonalized for the individual. For example, personalization of a tonemay be based on a demographic, a medical history, an emotional state, aprogress, a location, a profile, or an overall score of the individual.For example, a profile of an individual may indicate that he/sheresponds best to sharp but constructive criticism, so the tone of thefeedback or instructions may reflect this individual preference. Furtherfor example, the individual may have a medical history of depression ormood swings, so the tone of the feedback may be exceptionally upliftingand positive to reflect this individual preference. Still further forexample, the user may be extremely close to reaching his/her targetoverall score, so the tone of the feedback may be encouraging andempowering.

In some embodiments, the notification may include an overall score (asdescribed elsewhere herein), a compliance score (as described elsewhereherein), or a compliance rating of the individual relative to one ormore peers. The notification may be based on one or more of theplurality of parameters (e.g., exercise compliance, medicationcompliance, patient report inputs, patient generated health data, etc.),as described elsewhere herein. The notification may be displayed on adisplay of a mobile computing device of the individual. The compliancerating is based on a comparison of the overall score to an expectedoverall score for the individual. The expected overall score of someembodiments is based on projections of how the measured parameters ofthe individual will change over time if he/she executes the prescribedexercises or other instructions. The expected overall score may beupdated over time as the sensor system measures the parameters and thesystem tracks user progress over time. Alternatively, the expectedoverall score may be based on a goal set by the individual or ahealthcare provider of the individual, for example based on a target orgoal for the individual. In some instances, the overall score maypredict a time range for recovery. In further instances, a time rangefor recovery may be based on adherence and/or patient reported outcome(PRO) data, and/or patient generated health (PGHD) data.

In some embodiments, instead of or in addition to sending thenotification to the individual, the monitoring system transmits anotification to a caregiver, healthcare provider, family member, friend,and/or peer. This notification may also include a compliance rating ofthe individual, as shown at block 1150 of FIG. 11 . The notificationprompts one or more of the caregiver, healthcare provider, familymember, friend, and peer to contact the individual and/or healthcareprovider to encourage the individual to adhere to his/her prescribedcare instructions and to provide a notice to one or more of: thecaregiver, healthcare provider, family member, friend, and peer.

Sensor System

A functional block diagram of one embodiment of a sensor system isprovided in FIG. 3 . While numbered uniquely, one skilled in the artwill appreciate that the sensor system 110 of FIG. 1C may be formed ofany embodiment of a sensor system described herein and may include anyof or all the functional components described with respect to the sensorsystem 300 shown in FIG. 3 . Moreover, although illustrated separately,it is to be appreciated that the various functional blocks of the sensorsystem 300 need not be separate structural elements.

The sensor system 300 of various embodiments includes a stretchablecomponent 310 configured to adhere to or fit securely around the bodyportion, and a sensor module 320 coupled thereto. In some embodiments,at least a portion of the sensor module 320 is removable from thestretchable or adhesive component 310. For example, the stretchable oradhesive component 310 may be formed of a machine-washable fabric, andat least a portion of the sensor module 320 may be housed within aprotective casing that is detachable from the stretchable or adhesivecomponent 310. In some embodiments, a first portion of the sensor module320 is integrated into the stretchable or adhesive component 310 while asecond portion is positioned within the protective casing. For example,a processing unit 330 and a battery 350 may be stored within theprotective casing, while a strain gauge, resistor, and/or other sensingcomponents 360 of the sensor module 320 may be weaved into, disposedwithin, printed on, affixed to, or otherwise integrated into the fabricof the stretchable component 310.

In some instances, sensors are configured to be worn for long durations;in other instances, sensors may be configured to be worn for shortperiods. Duration of wearing may depend on the time to track specificexercises. Specifically, in some embodiments, sensors may be worn forover 3 months and/or for the entire duration of a clinical episode ofcare. Sensors may be worn for a fixed duration (e.g., greater than 2weeks, a month, two months, three months, six months, etc.) without theneed for recharging thus allowing for greater usability and safety.

Sensors may be configured to integrate audio or haptic feedback (e.g.vibration) technology to provide patients with feedback as they achievea pre-configured limit for a range-of-motion, and/or for the performanceof on-going assessments.

As shown in FIG. 3 , the sensor module 320 includes a processing unit330, which may be a general-purpose microprocessor, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an applicationspecific integrated circuit (ASIC), or other programmable logic device,or other discrete computer-executable components designed to perform thealgorithms and functions described herein. The processing unit 330 mayalso be formed of a combination of computing devices, for example, a DSPand a microprocessor, a plurality of microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suitableconfiguration.

In various embodiments, the processing unit 330 is coupled, via one ormore buses, to the memory 340 in order for the processing unit 330 toread information from and write information to the memory 340. Theprocessing unit 330 may additionally or alternatively contain memory340. The memory 340 can include, for example, processor cache. Thememory 340 may be any suitable computer-readable medium that storescomputer-readable instructions for execution by computer-executablecomponents. For example, the computer-readable instructions may bestored on one or a combination of RAM, ROM, flash memory, EEPROM, harddisk drive, solid state drive, or any other suitable device. In variousembodiments, the computer-readable instructions include applicationsoftware 345 stored in a non-transitory format. The software, whenexecuted by the processing unit 330, causes the processing unit 330 toperform one or more operations described elsewhere herein.

In various embodiments, a power supply, such as a battery 350, iselectrically coupled to provide power to the processing unit 330 andother electronic components. The battery 350 may be rechargeable ordisposable. Additionally, some embodiments of the sensor module 320 mayinclude one or more signal processing components, such as a filter(e.g., low pass, high pass, or band pass filter), an amplifier, and/oran analog-to-digital (AD) converter.

As shown, the sensor module 320 includes one or more sensors 360configured to detect parameters indicative of the monitored individual'shealth. For example, the sensor module 320 includes a circumferencesensor 361 configured to detect changes in the circumference of the bodyportion. The circumference sensor 361 may detect a change incircumference indirectly. For example, when the body portion expands incircumference, the stretchable component 310 positioned around the bodyportion experiences an increase in tensile stress that causes strain(i.e., a physical deformation) in the stretchable component 310. In someembodiments, the circumference sensor 361 includes an electricalcomponent positioned on or embedded within the stretchable component310. The electrical component may itself experience strain in responseto the increased tensile forces in the stretchable component 310. Thisincreased strain in the electrical component changes the electricalconductance, and thus, the inductance, resistance, and/or capacitance ofthe component in a known, predictable manner. Thus, the strain can becalculated from a detected change in inductance, resistance, and/orcapacitance of the electrical component. In turn, the processing unit330 is configured to calculate a change in circumference from thedetected change in inductance, resistance, and/or capacitance and obtainan absolute or relative circumference measurement. The electricalcomponent may be a foil strain gauge, semiconductor strain gauge (e.g.,a piezoresistor), a nanoparticle-based strain gauge, a capacitive straingauge, any other resistor, or any other suitable electrical componentthat experiences a detectable change in electrical properties inresponse to strain.

In some embodiments, the sensor module 320 additionally includes one ormore of: an orientation sensor 362, a motion sensor 363, a temperaturesensor 364, an image sensor 365, an angle sensor 367, one or morecardiovascular sensors 366, and a magnetometer sensor 369. Theorientation sensor 362 of some embodiments is a gyroscope configured todetect when the body portion has tilted, been elevated, or otherwisechanged position. The motion sensor 363 of some embodiments is anaccelerometer configured to detect changes in motion such as repetitivechanges in motion indicative of exercise. The temperature sensor 364 ofsome embodiments is a thermistor, thermometer, or othertemperature-responsive sensor configured to detect changes in skintemperature at the body portion. The image sensor 365 of someembodiments is a camera, semiconductor charge-coupled device (CCD), orcomplementary metal-oxide-semiconductor (CMOS) configured to detectchanges in the attenuation of light waves indicative of changes in skincolor at the body portion. The angle sensor 367 of some embodiments isan inclinometer, clinometer, or goniometer that measures angles of slope(or tilt), elevation, or depression of an object with respect togravity, for example to measure a range of motion of a body portion. Themagnetometer sensor may measure the movement of a body portion relativeto the magnetic field of the earth. In some instances, a magnetometersensor may generate a pattern during use by the patient while performingan exercise on a body portion to which the sensor is attached. The oneor more cardiovascular sensors 366 may include, for example, a pulseoximeter, a plethysmograph sensor, a pulse rate monitor, and/or a heartrate monitor. Exercise performed by a user may generate a signal fromany of the preceding sensors that may be correlated with an idealpattern to determine if the exercise was performed properly (e.g.,proper form, duration, etc.) and the number of times an exercise wasattempted as well as the number of times it was performed properly.

In various embodiments, some of or all the measurements obtained by thesensor system 300 are transmitted wirelessly, for example, via acommunication antenna 370, to the mobile computing device 120 forprocessing, analysis, and storage. The communication antenna 370 may be,for example, a transmitter or a transceiver. The measurements may beautomatically pushed to the mobile computing device 120 or retrievableby a request from the mobile computing device 120. In other embodiments,some of or all the measurements are processed, analyzed, and stored onthe sensor system 300.

Various, non-limiting embodiments of the sensor system 300 are providedin FIGS. 4A-4K. As shown, each sensor system 300 includes a stretchableor adhesive component 310 and a sensor module 320. While the sensormodule 320 may be largely positioned within a protective casing thathouses many of the electrical components, at least a portion of thecircumference sensor extends outside of the casing and is positioned toexperience strain induced by the circumference of the body portion. Insome embodiments, such as the embodiments of FIGS. 4A-4B, the sensorsystem 300 is formed of a strap, band, adhesive strip, or belt. Theentirety of the strap, band, adhesive strip, or belt may be deformableand circumferentially stretchable, or only a portion of it may beconfigured to stretch. The strap, band, adhesive strip, or belt may besized and configured for placement on an upper torso or chest or lowertorso or waist, as in FIG. 4A. Alternatively, it may be sized andconfigured for placement on a limb, such as an upper arm, lower arm,upper leg, or lower leg, as in FIG. 4B.

In various embodiments, the sensor system 300 is removable andconfigured for repeated reattachment. In order to achieve consistent,reliable, and accurate results, it is desirable for the various sensorsto be located at the same locations with each reattachment. Tofacilitate proper positioning of the sensors, in some embodiments, thesensor system 300 is integrated into clothing, footwear, a band, or abrace. For example, one or more stretchable components 310 and sensormodules 320 may be integrated into a shirt (FIG. 4C), sports bra,shorts, leggings or pants (FIG. 4D), underwear, compression socks orother socks (FIG. 4E), partial socks or sleeves (FIG. 4F), knee brace(FIG. 4G), ankle brace (FIG. 4H), or any other suitable garment.Placement of the sensors may be configured to precisely track thelocalized movements, position, and orientation of that body part orjoint. In some embodiments sensors must be flexible enough to be worn atany joint in the body to track motion at any of the axes. In furtherinstances, sensors may be configured such that they may be worn on aspecific joint or location of the body.

In some embodiments, including any of the embodiments described withrespect to FIGS. 4A-4K, at least a portion of the sensor module 320 isremovable. This is illustrated, for example, with the band provided inFIG. 4I and the leg sleeve/tube provided in FIG. 4J. The removableportion of the sensor module 320 may be securable to the stretchablecomponent 310 via any suitable attachment mechanism. For example, thestretchable component 310 may include a cradle or holder sized toreceive the removable portion of the sensor module 320, and theremovable portion of the sensor module 320 may snap or clip into theholder, as shown in FIG. 4I. Alternatively, the removable portion of thesensor module 320 may zip or hook into place, or it may slide betweenlayers or into a pocket of the stretchable component 310, as shown inFIG. 4J. In some such embodiments, the stretchable component 310 iswashable. In some embodiments, the removable portion of the sensormodule 320 is enclosed in a water-resistant or water-proof protectivecasing. In some embodiments, the removable portion of the sensor module320 may house the processing unit 330 and any associated electricalfiltering and processing components, the battery 350, an accelerometer,a gyroscope, and/or one or more additional parameter sensors. In someembodiments, the removable portion is interchangeable and configured forattachment to a plurality of garments and devices. In some embodiments,the removable portion is automatically activated upon attachment to agarment or automatically deactivated upon detachment from a garment.

In FIG. 4K, the portion of the sensor 361 that is integrated into thestretchable component 320 is schematically drawn with visible lines toimprove understanding. One skilled in the art will appreciate that eachof the sensor systems 300 provided herein may have a similar feature,but in FIGS. 4A-4J, such a feature is not visible from the outside ofthe device. The depicted circumference sensor 361 may be any straingauge or other suitable device described elsewhere herein. Additionally,as shown in FIG. 4K, any of the sensor systems 300 of FIGS. 4A-4K mayinclude multiple circumference sensors 361 (e.g., two, three, or moresensors) in order to sense a parameter indicative of circumference at aplurality of locations of the body portion.

In various embodiments, for example as shown in FIG. 4L, sensors may usea magnetic mounting system so that localized body motion,circumferential width increase, orientation, and/or localizedtemperature variations may be detected. In some embodiments, the magnetis inside the sensor and attached to a magnetized adhesive strip whichmay be placed anywhere on the body. In such embodiments, the sensor maybe used to precisely track the localized movements of that body part orjoint.

In various embodiments, the sensor may be built using flexibleelectronics to improve the attachment capability and allow more flexibleand accurate movement tracking. Alternatively or additionally, a sensormay comprise an adhesive strip and a magnetic material (e.g., notmagnets themselves) placed inside or glued to an antibacterial singlesided or double-sided adhesive. In further embodiments, theantibacterial single sided or double-sided adhesive may comprise one ormore non-stick surfaces, padded regions, or segments of differingdegrees of stretch or stiffness. Stretchiness or stiffness may bedefined as a parameter of force divided displacement, with ranges ofstretchiness, stiffness, or tensile modulus. Exemplary ranges ofmaterial properties for adhesive or materials comprising adhesive mayinclude modulus of elasticity in the range of 0.001 to 24 GPa. Magnetsinside the sensor and/or the magnetic adhesive strip may provide theneeded adhesion to attach the sensor to the body. The adhesive portionof the magnetic adhesive strip may be configured to stay attached to theskin for minutes, hours, days, weeks, months, or years. The adhesiveportion of the magnetic adhesive strip and/or the magnetic adhesivestrip itself may be configured to withstand exposure to water, wind,abrasion, etc. The tackiness of adhesives used to generate an adhesivestrip may comprise a range of between 0.1 to 15 N as measured by a tacktester.

In some embodiments, the magnetic adhesive strips may be provided to theuser in packs or sets for placement at different locations of the body,and or in packages or sets that facilitate ease of use and adoption bythe patients of the system.

In various embodiments, the magnetic adhesive strips may be disposableinstead of reusable, and the patient may replace the previous magneticadhesive strip with a new one after a lifecycle of use of the magneticadhesive strip is complete. In some embodiments, a lifecycle of use maybe on the order of minutes, days, weeks, months, or years. In somemethods, for using a magnetic adhesive strip, the user may replace themagnetic adhesive strip at a rate or increment that is predetermined(e.g., every day, every week, every other week, once a month, etc.).

In various embodiments, as shown in FIG. 4L, a patient wearing thesensor may be asked to execute exercises targeted for a specific musclegroup and/or joint. Sensors may be configured to track planar movementsof the body across sagittal, transverse, and coronal planes. In suchembodiments, tracking of all types of movements and measures of flexion(e.g., degrees of flexion) may occur, including for example flexion,extension, abduction, and adduction. In some instances, tracking may beperformed for exercises prescribed by a physical therapist, for example,a long arc quad extension done while sitting on a chair (as shown inFIG. 4L) captures knee extension from 90 degree (bent) to 0 degree(straight).

Additionally or alternatively, sensors may be configured to track speedof movement, length or times of the duration over which the user (e.g.,patient or athlete) was able to hold a strenuous position (e.g., astretch, a lift, plank, etc.). Metrics measured from one or more sensorsmay be used to compute, assess, or measure the user's strength and/orendurance.

In additional or alternative embodiments, sensors may be configured tomonitor or assist in the assessment or performance of isometricexercises (e.g., exercises that do not involve movement but do involveexercising the muscles). In such instances, the sensor may be configuredto track the duration of a stretch and whether the body part shook aspart of executing the stretch. Accurate detection of vibratory movementsmay be used to calibrate, compute, assess, or measure the strength ofthe joint and/or muscle group involved in performing the exercise.

Sensors may be dynamically calibrated to measure the smoothness ofmovement during movement or exercise. In some instances, the sensors maybe used to measure a body portion, as shown in FIGS. 4M and 4N. Asshown, the sensors are able to detect the performance of a jointextension performed two different ways. In FIG. 4M the patient performsa typical joint extension and in FIG. 4N the patient performs the jointextension with a 3 second hold at 60 degrees ROM, the maximum range ofmotion for the exercise. The sensors are able to detect and distinguishbetween the different ways of performing the exercise, illustrated hereas the sharp peaks in FIG. 4M indicate that the direction of bodyportion motion changes shortly after reaching 60 degrees ROM, whereasthe data in FIG. 4N shows that peaks flattening out at 60 degrees ROM.Additionally, the sensors in FIG. 4N can detect shaking of the userduring the hold period, which can be used to determine the strength ofthe body portion used in performing the exercise. Data including whetherthe exercises were performed, how they were performed and features ofthe performance (e.g., shakiness, speed, smoothness, etc.) may be usedby the system to dynamically calibrate the performance of the exercise(e.g., according to flexibility, strength, and endurance) and impact onthe prognoses and recovery trajectory. In some instances, thesophistication or degree of difficulty for the exercises in a patient'scare plan may be dynamically adjusted based on the performance measuredby the sensors. Sensors are equipped to make adjustments and identifyflexibility, strength, and endurance of the patient at resolutions fargreater than what would be obvious to a trained eye. Sensors may be usedto measure both the quality of movement as well as the quantity ofmovement. Each exercise in a care plan may have a separate tuning, withweaker muscles displaying jerkier motions and difficulty of exercisesrequested of the patient adjusting according to the actual performanceof the patient. The rate of which data is collected and tuned may bedynamically adjusted based on one or more of: the previous performanceof the user, the type of exercise being performed, and the battery lifeof the sensor or other hardware associated with the sensor including themobile device running the application.

Sensors may detect movement of a body portion in all three planardirections (e.g., X, Y and Z). FIG. 4N illustrates data collected formotion of the leg portion during standing hip abduction (see FIG. 4Lsecond exercise from right). In this exercise, the leg is extended awayfrom the body and FIG. 4N shows sensor data for the leg movement inthree planes. During the exercise, the leg was moved in the X direction(second panel from the top), however movement is also detected in the Y(third panel from the top) and Z direction (bottom panel). Sensors maybe equipped to detect slight and voluntary as well as involuntarymovements in the coronal, sagittal and transverse planes. Detection ofslight movement of joints that are supposed to be stationary and are notbeing used during an exercise are detected by the sensors and recordedand identified by the monitoring system. In some instances, theserecordings may be integrated into changes in the care plan, estimates ofprognosis/recovery, and/or transmitted to healthcare providers. Someexercises, such as standing hip abduction have a few degrees ofmovement, making them hard to measure in the presence of other movements(e.g., movement of joints that are supposed to be stationary). Themonitoring system comprises an algorithm with inbuilt thresholds thatmust be met before a repetition is designated as complete. Thresholdsmay be programmed separately for each exercise and may take into accountthe likely movement of other joints. The algorithm may also haveadaptive goals, wherein the thresholds are relaxed in the first few daysafter an operation and then slowly advanced. Thresholds may be adjustedaccording to one or more features including but not limited to: positionof patient in recovery process, user profile, care plan, indications ofother conditions or compensatory muscle or joint issues, and painlevels.

FIG. 5 depicts one example of a method 500 performed by the sensorsystem 300 described elsewhere herein. As shown at block 510, the methodincludes calibrating the sensor system 300 to establish a parameterbaseline. When the sensor system 300 is first positioned on anindividual, the stretchable or adhesive component 310 will experiencesome degree of strain. As described above, strain, position, or otherchanges may be detectable by a sensor 361, which is formed of anelectrical component positioned on or embedded within the stretchablecomponent 310. The electrical component may itself be deformable inresponse to tensile stress or movement and may experience a change inelectrical properties when deformed or moved, resulting in a change in aparameter such as the inductance, resistance, and/or capacitance of theelectrical component. Following positioning of the sensor system 300 onthe body portion, the initial parameter reading is set as the baseline.In some embodiments, the processing unit 330 calculates an actualbaseline strain, position, and/or circumference measurement, setting thebaseline to that value. In some embodiments, the baseline could beindividual specific (e.g., my range of motion today vs. yesterday) or atthe population level (e.g., the cohort of patients in recovery). Inadaptive goaling or other calibrations, the baseline can be adjustedperiodically, on-demand or automatically. In other embodiments,measurements are relative, and the initial parameter readings establishthe zero value. As part of the calibration process, the sensor system300 of some embodiments performs a status check. For example, in someembodiments, the processing unit 330 of the sensor module 320 checks toensure each sensor is operational; in some embodiments, it determineswhether the stretchable component 310 is experiencing a level of strainbelow a safety threshold. In some embodiments, the sensor system 300 ora mobile computing device communicatively coupled thereto is configuredto generate an alert if the sensor module 320 detects that thestretchable component 310 is too tight. In some embodiments, the sensorsystem 300 includes a vibrational element configured to generate hapticalerts.

At block 520, the method 500 includes detecting a change in a parameterthat correlates to, and is indicative of, a change in position,orientation, or circumference. The sensor system 300 is configured to beworn by the monitored individual over a period of time, for example,from several hours to several weeks. During that time, the inductance,resistance, capacitance, and/or other parameter of the position orcircumference sensor 361 changes as the position or circumference of thebody portion changes. These changes in the parameter of the position orcircumference sensor 361 may be detected, filtered, and processed by theprocessing unit 330 continuously or repeatedly, for example, at regularintervals.

At block 530, the sensor system 300 obtains a circumference measurementfrom the detected change in the parameter. The circumference measurementis calculated by the processing unit 330 using pre-programmedrelationships and equations, for example correlating circumferenceand/or strain to the detected change in a parameter. For example, insome embodiments, strain is calculated using the equation:

${{GF} = \frac{\frac{\Delta R}{R}}{\varepsilon}},$

where ε=strain; ΔR=change in resistance; R=initial resistance; andGF=gauge factor, which is a predefined value. Additionally oralternatively, in some embodiments, a cross-sectional area of the bodyportion may be determined using the equation:

${R = {\rho\frac{L}{A}}},$

where R=resistance; ρ=the specific resistivity, a predefined value;L=length of the electrical component (e.g., length of the resistor); andA=the cross-sectional area of the body portion. Circumference can bedetermined, for example, relying on the above equation and therelationships: Area=πr², and Circumference=2πr, where r=the radius. Fromthese and/or other pre-programmed equations, a change in circumferencecan be determined from a change in resistance, inductance, orcapacitance. The calculated circumference measurement may be an absolutemeasurement or a relative measurement providing a change incircumference relative to the baseline or other previous circumferencemeasurement.

The processing unit 330 of various embodiments calculates measurementsrepeatedly, for example, at regular intervals. In some embodiments, theinterval length varies based on the measurements. For example, theprocessing unit 330 may begin calculating measurements at a firstinterval (e.g., every hour). When a change in circumference,orientation, or position is detected, the processing unit 330 maytransition to calculating circumference, orientation, or positionmeasurements at a second, more frequent interval (e.g., every 15minutes) to enable closer monitoring of the circumference, orientation,or position. By obtaining circumference, orientation, or positionmeasurements frequently over time, the sensor system 300 is able toidentify and exclude anomalous measurements, for example, those causedby movement, muscle flexing, or noise.

As shown at block 540, in some embodiments, the sensor system 300 mayoptionally acquire measurements from one or more additional parameters.For example, one or more of the orientation sensor 362, motion sensor363, temperature sensor 364, and image sensor 365 may sense changes inone or more parameters, and the changes may be detected, filtered, andprocessed by the processing unit 330. For example, one or moreadditional parameters may be used to calculate a range of motion of abody portion of the user, as described in further detail elsewhereherein.

As shown at block 570, in various embodiments, the antenna 370 of thesensor system 300 transmits the circumference, orientation, or positionmeasurements and any other acquired measurements from the processingunit 330 of the sensor system 300 to a mobile computing device 120. Inorder to successfully transmit the measurements, in some embodiments,the sensor system 300 first determines whether a communicationconnection exists between the sensor system 300 and the mobile computingdevice, as shown at block 550. If the sensor system 300 and mobilecomputing device are not connected, the sensor system 300 stores themeasurements and data in memory 340 in the sensor module 320 until aconnection is established, as shown at block 560. When a communicationconnection does exist, the antenna 370 may transmit the measurements anddata upon establishing the connection, upon receiving new measurements,at a programmed interval, or when requested by the mobile computingdevice 120. In some embodiments, the mobile computing device 120receives and analyzes the measurements (as described in more detailbelow), and based on the measurements, the mobile computing device 120transmits instructions back to the sensor system 300 instructing thesensor system 300 when and/or what measurements should next be queried,processed, or transmitted.

In addition to or alternatively, the monitoring system calibrates thesensor system by determining an orientation of a sensor system, as shownin the method 1200 of FIG. 12 . At block 1210, the method 1200 includespositioning the sensor system around or on a body portion, for example aleg (e.g., leg, calf, knee), an arm (e.g., bicep, forearm, elbow), atorso, an ankle, a wrist, or a chest. At block 1220, the monitoringsystem recommends that the individual performs at least one of arecommended movement of the body portion or a recommended orientation ofthe body portion.

At block 1230, the method includes receiving measurements for aplurality of parameters of the body portion. Before, during, and/orafter the movement or orientation, the gyroscope and/or accelerometer ofthe sensor system measures one or more of the movement and orientationof the body portion in space to determine whether the sensor system isoriented properly or at least how the sensor system is oriented relativeto the body coordinate system of the individual (i.e., registering thesensor system to the body coordinate system). For example, if the systemdirected the individual to perform a bicep curl, the measurementsperformed by the sensor system should reflect an anatomically correctand feasible bicep curl (i.e., moving a forearm towards the bicepinstead of towards the tricep). In some embodiments, the sensor systemfurther includes an angle sensor, for example an inclinometer, tomeasure an angle of a joint or a tilt of a body portion to assist incalibrating the sensor system or determining an orientation of thesensor system on the body portion.

At block 1240, the method includes calculating a sensor systemorientation based on the measurements of the plurality of parameters. Insome embodiments, calculating includes comparing an actual motion ororientation of the body portion to an expected motion or orientationbased on the recommended motion or orientation. Further, in suchembodiments, the difference between actual and expected is compared to arange of values or a threshold for the recommended motion ororientation. If the difference is outside of the range of values orexceeds or does not reach the pre-determined threshold, the sensorsystem is determined to be oriented improperly. At such time, themonitoring system may notify (e.g., visually, audibly, haptically) theindividual to adjust the placement of the sensor system or the systemmay register the improper orientation and save it as an updatedorientation of the sensor system. The calculations may be performedlocally on the sensor system or remotely on a mobile computing device,network computing device, or supervisor computing device.

Muscular skeletal care devices or systems (e.g., monitoring system)comprise an application (e.g., mobile application) that may be utilizedto facilitate interaction between the user, optionally a health careprovider or administrator, and the sensor. The application may beconfigured to display a status of a particular exercise regime (e.g.,exercises prescribed, exercises completed, when, how well, etc.). Insome embodiments, users may interact with the application to execute agiven exercise or a selected exercise. Alternatively, patients may tapthe sensor to interact with a companion application (e.g. on a mobiledevice) to indicate completion of an assigned exercise and to move on toanother exercise (e.g., assigned to or selected by the user). Anapplication for interacting with the sensor may provide visual and/orauditory feedback to a user on an assigned or selected exercise that wascompleted by the user or remains to be completed by the user.

Mobile Computing Device

FIG. 6 provides a functional block diagram of one embodiment of themobile computing device. While numbered uniquely, one skilled in the artwill appreciate that the mobile computing device 120 of the system 100may be formed of any embodiment of a mobile computing device describedherein and may include any of or all the functional components describedwith respect to the mobile computing device 600 of FIG. 6 . Moreover,although illustrated separately, it is to be appreciated that thevarious functional blocks of the mobile computing device 600 need not beseparate structural elements.

The mobile computing device 600 of various embodiments includes aprocessor 610, for example, a general-purpose microprocessor. Theprocessor 610 is coupled, via one or more buses, to the memory 620 inorder to read information from and write information to the memory 620.The memory 620 may be any suitable computer-readable medium that storescomputer-readable instructions for execution by computer-executablecomponents. In various embodiments, the computer-readable instructionsinclude software stored in a non-transitory format, some such softwarehaving been downloaded as an application 622 onto the memory 620. Theprocessor 610, in conjunction with the software stored in the memory620, executes an operating system and the application 622. Some methodsdescribed elsewhere herein may be programmed as software instructionscontained within the application 622 stored in the memory 620 andexecutable by the processor 610.

In various embodiments, a power supply, such as a battery 630 isincluded within the mobile computing device 600 and is electricallycoupled to provide power to the processor 610 and other electroniccomponents. The battery 630 may be rechargeable or disposable.

In various embodiments, power utilization and hence longevity of thesensor may be improved by embedding computational algorithms to adjustor reduce activity of the sensor or other components of the system thatare needed to perform the assessment and/or recording or monitoring ofactivity. Algorithms for conserving power may be embedded in anycombination of: the sensor, a smartphone app, and/or in the cloud (e.g.,algorithms anywhere, cloud based care cloud). In some embodiments,sensor life may be extended by distributing the algorithms andoptimizing pairing between the sensor and phone communication protocols.

In various embodiments, the sensor's processing abilities and powerutilization are configured such that the sensor may embed range ofmotion and exercise repetition detection algorithms that accuratelydetect angular movements of human joints without needing an app as acompanion to the sensor. In such embodiments, data may be directly sentto cloud-based computing infrastructure for analysis (e.g., cloud-basedcare cloud).

For example, in scenarios such as lying down exercises, the user may notbe able to reach the mobile computing device and use the GUI. Algorithmsin the sensor can detect a user tapping on it (e.g., to indicate startof next exercise or activity) vs. using movement patterns for anadditional feedback.

In various embodiments, the sensor's physical characteristics arefurther optimized for use by patients in recovery with limited mobility.In such embodiments, the sensor has a body contour-fitting exteriordesign and body-friendly material (e.g., silicone) for comfort-wear.Designs may incorporate customized shape and asymmetrical weighting ofsensor(s) to ensure that sensors do not roll away from users/patients ifthe user/patient were to experience a fall.

The mobile computing device 600 of various embodiments includes aplurality of interfaces, such as a user interface 640 and a wirelessnetwork interface 650. The user interface 640 may include one or moreinput/output (I/O) devices. In some embodiments, the user input deviceincludes one or more of a button, switch, touchscreen, and keyboard, andthe output device includes one or more of a display screen, lightdisplay, audio output, and haptic output. The wireless network interface650 of some embodiments includes a receiver and transmitter forbi-directional communication. The receiver receives and demodulates datareceived over a communication network. The transmitter prepares dataaccording to one or more network standards and transmits data over acommunication network. A communication antenna in the form of atransceiver may act as both a receiver and a transmitter. In someembodiments, the mobile computing device 600 includes a plurality ofnetwork interfaces 650, including a first network interface configuredfor communication with the sensor system 300 and a second networkinterface configured for communication with a network computing device130.

In various embodiments, a health monitoring application 622 may comprisean intelligent application on a mobile computing device. The intelligentapplication may be configured to engage patients in their recovery andhelp facilitate adherence to a prescribed health care plan using one ormore modalities of digital engagement. In some embodiments, the muscularskeletal system may rely on one or more of the following methods toensure patient engagement and adherence: establish a daily routine,reinforce achievable goals, focus on action instead of history,personalization, and developing a consistent personality profile of thepatient. A personality profile for the patient is generated frombehavioral factors of the patient, including adherence to pre-operativeinstructions, the extent to which the patient is engaged with therecovery program via the app, the amount of exercises performed by thepatient, etc.

In various embodiments, a health monitoring application 622 isdownloaded from a network computing device 130 onto the mobile computingdevice 600 by the monitored individual. The health monitoringapplication 622 may include one or more of a user interaction module 624and a data processing module 626.

The user interaction module 624 of various embodiments instructs themobile computing device 600 to request information from, and provideinformation to, the monitored individual. The user interaction module624 includes a graphical user interface displayable on a screen throughwhich the monitored individual can interact with the monitoring system.The monitored individual may also interact with the user interactionmodule 624 through audio and/or verbal inputs and outputs. For example,in some embodiments, the user interaction module 624 generates soundsthrough which the monitoring system can provide instructions and/orinformation to a monitored individual and query the monitored individualfor information. In some embodiments, voice recognition capabilitiesallow a monitored individual to verbally respond to requests forinformation.

One non-limiting example of a graphical user interface generated by theuser interaction module 624 is provided in FIG. 7A. As shown at FIG. 7A,in various embodiments of the monitoring system, upon downloading thehealth monitoring application 622 onto a mobile computing device 600, alogin screen prompts the monitored individual for login credentials,including, for example, a username and password. The monitoring system100 of various embodiments is configured to be secure, requiring everyuser of the system (e.g., including monitored individuals, supervisors,reviewers, and system administrators) to enter proper login credentialsdemonstrating authorization to use the system prior to interacting withthe monitoring system. Following the initial download of, and log in to,the health monitoring application 622, the mobile computing device 600may perform a method to search for, and communicatively pair with, anearby sensor system 300. In future uses of the system, the mobilecomputing device 600 may automatically pair with the same sensor system300 with which it previously communicated and may allow a monitoredindividual to provide and receive information related to data beingacquired from the specific paired sensor system 300.

In some embodiments, upon logging into the health monitoring application622 for the first time, the monitored individual is prompted to providebiographical information and/or a medical history. For example, the userinteraction module 624 may prompt the monitored individual to enter oneor more of a: name, identification code, gender, sex, date of birthand/or age, ethnicity, race, height, weight, and medications and/orsupplements routinely taken. The information requested by the userinteraction module 624 varies depending on the intended use of themonitoring system. For example, in some embodiments, the monitoringsystem is used for wellness purposes to track changes in circumferenceof a body portion caused by changes in weight, muscle mass, and/or fetaldevelopment. Depending on the intended use, the user interaction module624 may modify its prompts, for example, in order to request that themonitored individual enter one or more of the following when relevant:desired fitness goals, desired weight loss or weight gain goals, currentlevel of fitness, average amount of exercise performed, gestation age,etc. In other embodiments, the monitoring system is used for healthcarepurposes to monitor for changes in circumference of a body portioncaused by abnormal swelling. If the monitoring system was prescribed toan individual for use before or following surgery, the user interactionmodule 624 may request that the monitored individual enter informationon the type of surgery and the date of surgery. Additionally oralternatively, in some embodiments, the user interaction module 624generates prompts requesting that the monitored individual enter in riskfactor data relevant to assessing the likelihood that the monitoredindividual will develop complications, for example swelling of the bodyportion. In some embodiments, the user interaction module 624 requeststhat the monitored individual select any clinically-relevant riskfactors that apply to the individual. The risk factors may be presentedin a list and may include, for example, one or more of: duration ofinjury, chronic or acute, pre- and post-surgical performance, surgicalfindings, paralysis, paresis, plaster immobilization of a limb, activecancer and stage of cancer malignancy and treatment, previous history ofdeep vein thrombosis (DVT) and/or pulmonary embolism (PE), familyhistory of DVT and/or PE, obesity, history of smoking, heart disease,lung disease, inflammatory bowel disease, recent childbirth, pregnancy,blood clotting disorder, advanced age (e.g., over 70 years of age),and/or use of supplemental estrogen or birth control pills.

Additionally or alternatively, in some embodiments of the monitoringsystem, the user interaction module 624 prompts the monitored individualto enter in information related to a current health or wellness statusand/or current or recent habits and activities. For example, the userinteraction module 624 may request that the monitored individual enterin information related to one or more of: symptoms, a wellness rating, apain rating, an exercise performed, a food consumed, a supplementconsumed, a medication administered, a duration of sleep attained, andan indication of whether the monitored individual has complied with aprescribed instruction. The user interaction module 624 may prompt themonitored individual for such information on a regular basis (e.g.,daily or hourly), upon each opening of the health application 622 on themobile computing device 600, or upon detection of a change in status(e.g., a change in a circumference measurement reading or a change inacceleration).

As shown in the non-limiting graphical user interfaces provided in FIGS.7B-7C, the user interaction module 624 is also configured to provideinformation to a monitored individual. For example, the user interactionmodule 624 may enable the monitored individual to review his or herpreviously-entered medical history, current or recent measurementsacquired from the sensor system, a history of the tracked measurementsacquired from the sensor system (as shown in FIG. 7B), and/or health orwellness information. In some embodiments, the user interaction module624 provides the monitored individual with access to a library of healthand wellness information, for example, to a library of informationmaintained by a third-party provider, such as WebMD® or the Mayo Clinic®(as shown in FIG. 7C). In some embodiments, the user interaction module624 provides the monitored individual with access to individual-specificinstructions customized by the monitored individual's health or wellnessprofessional (i.e., supervisor). The individual-specific instructionsmay include pre-operative instructions, post-operative instructions,instructions related to a diet or exercise regimen, or any otherinstructions the supervisor chooses to share with the monitoredindividual. The instructions may include, for example, notifications ofsuggested meals or exercises or reminders to sleep, exercise, elevatethe legs, limit exertion, or take medications or supplements. In someembodiments, the user interaction module 624 provides the monitoredindividual with access to instructional videos, for example, videosdemonstrating how to perform recommended or prescribed exercises or howto cook various recommended healthy meals.

In various embodiments, the user interaction module 624 also providesinformation to the monitored individual in the form of alert outputs.The alert outputs may be generated at a regular interval or upondetection of a change in circumference or other monitored healthparameter. The alert outputs may include notes of encouragement,notifications of progress, reminders of particularly relevantinstructions, or an instruction to contact a healthcare provider. Threenon-limiting examples of graphical user interfaces displaying alertoutputs are provided in FIGS. 7D-7F. In each of FIGS. 7D-7F, the alertoutput includes a numerical and pictorial indication of progress and amessage providing pertinent feedback. Additional users of the monitoringsystem, for example, supervisors and/or reviewers, may also be able totransmit messages to the monitored individual through the system, whichare displayable in the graphical user interface of the user interactionmodule 624.

Graphical user interfaces (GUIs) may be integrated as part of anapplication or app configured to adapt to human behavior training andneuroplasticity. Examples of customizable adaptive user interfaces areillustrated in FIG. 7G-7K. GUIs may be configured to adjust patientbehavior and assist in compliance with a required care plan. Themonitoring system and graphical user interfaces may be constructed todevelop a daily routine of self-care in accordance with a healthcareprovider's care plan, which triggers action and engagement on behalf ofthe patient in response to given contexts. Contextual attributes maycomprise any combination of: time of day, time in care episode (e.g.,before surgery, after surgery, two weeks after surgery, day beforesurgery, etc.), patient behavioral profiles, and adherence level to agiven care plan—for example, the monitoring system application may beconfigured to reduce a lack of adherence to an exercise protocol in thecare plan by sending appropriate triggers to patients to establish ormaintain engagement. Triggers provided by the monitoring system willvary based on the user/patient's psychological profile (e.g., motivated,disciplined) and will apply behavioral research paradigms such as theHawthorne effect, change curve, and/or other behavioral or psychologicalelements to make patients more accountable to their healthcareprescribed care plan. For example, the app may employ methods of theHawthorne effect by creating a system that may be monitored by anotherparty more specifically by making patient specific information visibleto care providers and caregivers and via generating contextuallypersonalized messages from an ecosystem of caregivers.

In various embodiments the monitoring system may generate a patientprofile. A patient profile may be built from sociological factorsincluding the patient's social network. Connecting patients to thenetwork, and integrating their network in a privacy compliant way with apatient's care team (e.g., family, friends, home care providers etc.)during recovery, may improve the one or more parameter of “socialadherence” that may be monitored by the system as part of an adherenceparameter and optimized for as part of the adaptive care plan. Anadaptive care plan comprises any combination of the dynamicallyadjustable care related instructions provided by or supported by one ormore individuals or entities external to the user/patient, includinghealthcare providers and supporting care providers (e.g., family,friends, home care providers, etc.). Depending on the patient profile,the system may be configured to prompt caregivers from the network toreach out to the patient periodically or when the patient's overalladherence level is low. Caregivers may choose to respond directly to thepatient or use automated mechanisms to respond (e.g., via chatbots) ifthey can't engage in real-time and live.

FIG. 7G illustrates how the app may be constructed to develop a dailyroutine of self-care in accordance with a healthcare provider prescribedcare plan. The interface in this example is connected to a care plancomprising a schedule of activities for the user. In this example, theuser is on Day 6 of Week 1 of the plan. The interface comprises 3panels, one for Dynamic Engagement, and two associated with the DailyCare Routine. The user has not yet completed the morning portion of theSelf-Care Session, which is scheduled for 9 AM. The Dynamic Engagementpanel prompts the user (e.g., the user's name is Agnes) to “tap below tostart” the morning session. A yellow circle comprising an arrow and thewords “start” are highlighted to show the user where to press. Thesecond panel associated with the Daily Care Routine is greyed outindicating that the user does not have access to this part of the DailyCare Routine, which in this example corresponds to an evening self-caresession scheduled for 6:00 pm. At the bottom of the screen is anindicator that provides the user with feedback about the connection ofthe sensor (e.g., if the sensor is connected or not). In some instances,the connectivity of the sensor may be required and the light may need tobe lit up before the user may activate the start sequence of theSelf-Care Session. In other instances, activation of the Self-CareSession may walk the user through connecting the sensor if the sensor isnot already connected.

In alternative or additional embodiments, the app may ask patients tomake an explicit commitment to their care sessions, for example to driveownership and autonomy. In alternative or additional embodiments, theapp may be configured to enable the patient to follow a believe-know-actcycle such that the app helps patients transition through the states inthis cycle. To get from “believing” to “knowing,” the app delivers aframing for the information and context that resonates and makesintuitive sense. To get from “knowing” to “acting,” the app may build incontext sensitive, personalized prompting mechanisms. Finally, to getfrom “acting” back to “believing,” the app may reinforce how the user'saction make a difference.

In some embodiments, the application may comprise a contextual dynamicworkflow with cloud integrated intelligence that may guide the patientor user based on user or patient specific controls (e.g., part of acloud based care cloud). The application may be customized to the needsand disposition of the user (e.g., patient, athlete, etc.) such that thepatient is lead through a self-managed care session that includes, forexample, physical therapy, medication management, pain control, andholistic care. The flow of the app utilized by the patient may beadjusted dynamically based on one or more contextual (e.g., time orsymptom dependent) parameters including: pre-operative readiness if thepatient is in pre-op (e.g., getting ready with activities, exercises,and appointments); periodic symptom checking via the app in definedincrements of time (e.g., 2 weeks) so that clinicians may intervenebased on symptoms, pain management, and medication adherence in adefined increment of time (e.g., 3 weeks post-surgery), or until painlevel is below a threshold and inflammation is in check; performance ofprescribed exercise routine only when pain and symptoms are not presentor of minimal concern.

FIG. 7H illustrates an interface and information flow behind theinterface, that is used to establish adaptive goaling and keep patientsengaged while making progress. The interface is configured to adjust tothe needs of the patient while also providing guidance as the patientperforms exercises. The display comprises information about the setsbeing performed and the user's current status in the process. In thisinstance, a green dot with a checkmark indicates that 3/3 exercises ofSet 1 have been successfully completed and the user has completed 1/3 ofthe exercise in Set 2. The user is currently engaged in the processbecause the button below the Set table provides the option of pausingthe routine. To the right of the pause button is a button that may beused to move on to the next exercise in the routine. On the right-handportion of the screen is an illustration depicting the range of motionfor the particular exercise being performed and the relative performanceof the user. In this case, the display shows the Week 1: Typical Rangeof Motion (ROM) Goal, the Week 1: Adaptive ROM Goal and the Actual ROMfor the user. In some embodiments, the performance of the user may becaptured and stored as part of the user's data on a cloud storage system(e.g., a care cloud). Captured data may comprise a range of the planarmovements of the body when the patient performs habilitation (e.g.,rehabilitation, pre-habilitation, etc.) exercises. Pre-habilitationexercises performed before a medical procedure may be detected,captured, monitored, and used to establish a point of reference orbaseline for the patient. Rehabilitation exercises performed after amedical procedure may be detected, captured, monitored, and used todetermine the current status and prognosis of the patient after theprocedure as well as to determine the range of motion of the patient asa means of adaptively generating a care plan with next goals and steps.Adaptive goal setting may enable a patient to pursue achievable range ofmotion goals as part of their recovery plan. Furthermore, adaptive goalsetting may prevent patients from getting dissuaded by standardizedgoals that may not apply to them. Pursuing goals that are over range oreasily attainable by the user, may cause users to drop off from theircare plan routine due to what they deem to be unnecessary (e.g., easilyachievable) or completely out of reach (e.g., unachievable).

Also shown in FIG. 7H is the information flow for a patient using themobile monitoring application and sensor containing device to performand receive adaptive guidance on their exercises. In this embodiment,the application is interacting with audio cues from the patient andproviding audio cues to the patient to facilitate effective performanceof the exercises. The application on the mobile device communicates viathe visual user interface and produces sounds of varying tone orduration based on the accuracy of the exercises being performed by theuser. As the user performs the exercises, the server in the care cloudanalyzes sensor data and provides vocal cues based on the user'simmediate performance, for example if the patient was performing thejoint extension exercises with the data shown in FIG. 4M-4O, then thecare cloud could dynamically assess changes in strength, flexibility,range of motion, etc. and provide instructions to the patient accordingto their immediate performance; for example, if the patient approaches60 degrees (see FIG. 4M/4N) appearing strong (e.g., has little movementin unintended planes (FIG. 4O), showing minimal shaking, etc.) then theapplication may provide instruction to the patient to hold the legposition at 60 degrees for 3 seconds; however, if the patient approaches60 degrees appearing weak then the application may provide some otherinstruction for example it may reduce the number of reps or change theexercise in another way. A chatbot may be used to provide theinstructions according to the patient actions as calculated by thesensor. A chatbot may also be used to respond to patients verbalquestions or commands, including for example verbal commands to start orstop the exercises, and questions about performance relative to previousdays, etc. Chatbots may comprise part of the monitoring applicationrunning on the mobile device, or external systems configured forinteracting with the mobile device or monitoring system. Chatbots cancomprise voice assistants including Amazon Alexa, Siri, Cortana, Googlevoice activated devices, etc.

As previously mentioned, the user interface may comprise adaptive alertoutputs. In some embodiments, alert outputs may be configured inresponse to a patient profile or one or more other features as shown inFIG. 7I. Adaptive alert outputs can comprise AI based game mechanics, ornext-best-decision based methods for encouraging user's or patients toadhere to their care regimen. In some instances, AI based game mechanicsmay offer a user or patient specific rewards for continuing to use thesystem. User or patient specific rewards may be based on a user profileor user model generated to identify and weight one or more factors thatinfluence the user's behavior and optimize for the desired user behavior(e.g., adherence and recovery). FIG. 7I illustrates a next-best-decisionflow for a series of patients based on information obtained from apatient named Bob who successfully complete his physical therapy.Knowing all facts about Bob, the model adjusts weights to calculate aprobability of a patient (e.g., Bob) attending physical therapy the nextday. That value is calculated as the weighted sum of one or morefactors. The one or more factors may include but are not limited to:number of exercise sessions or PT sessions attended, the progress inphysical therapy based for example on the number of exercises completed,the number of exercises completed with the target ROM, response tomotivational message, pain level, timeliness and regularity at specifictimes of the day, medication taken, medications taken and reported,medications not taken, physical condition, completion of checkliststoward maintenance of a safe environment including preparing theexercise environment by securing carpet etc., and communications withcaregiver and care team. Based on these factors and the correspondingweights known for Bob, probability of other patients (e.g., Agnes andLisa) is determined from a weighted sum computed based on theirrespective performance. Various scenarios are computed for each patient(e.g., Agnes and Lisa) and a best action is determined that willincrease the likelihood that Agnes and Lisa will complete their physicaltherapy that next day. The monitoring system then provides Agnes andLisa each with their customized reward or suggestion. In FIG. 7I, forexample, the system has computed that the best action for Agnes is tohave more icing and rest, and the best action for Lisa is a digitalreward in the app. The system adapts to each user specific trajectoryand user profile to provide a reward or recommendation that will bestsupport their successful movement through the care plan and towardsrecovery. Rewards or recommendations may comprise or rely on mechanismsdisclosed in FIG. 1A-FIG. 1B as well as additional rewards orrecommendations. Examples of rewards may include but is not limited to:posting the user at a position on a leader board; using digital goodssuch as a Hawaii theme, stars, balloons, etc. on the application;informational messages reminding the user of the importance ofmedication or physical therapy; notifications to do an activity based ona time limited period; and communications from caregivers or care team,such as a chat message or a call.

In various embodiments, a monitoring system may comprise a healthcareprovider interface. FIG. 7J and FIG. 7K illustrate exemplary userinterfaces and data flows for a physician or healthcare application. Ahealthcare provider interface may enable healthcare providers to set theuser (e.g., patient) up in the monitoring system with a care plan, forexample, an MSK care plan tailored to the needs and preferences of theuser (e.g., the patient, athlete, etc.). A healthcare provider interfacemay provide healthcare providers with the ability to customize theuser's care plans. In some embodiments, a healthcare provider interfacemay comprise easy to view patient-reported and patient generated data.Patient generated data may comprise data collected from one or moresensors and/or sensor containing devices utilized by the user. Ahealthcare provider interface provides healthcare providers with astatus of a user's progression along the provided care plan (e.g., amedically approved health care plan). Healthcare provider interfaces mayprovide healthcare providers with alerts if the patient crosses one ormore thresholds that have been set up by a healthcare provider forpatient-reported and patient generated data.

FIG. 7J illustrates components of a healthcare provider dashboard. Thedashboard is configured to support comparisons between the two or morecomponents of a PRO or PGHD data. In this example, the dashboardcomprises plots of the patient's medical adherence, pain level, exerciseadherence and range of motion. Each plot displays data generated frommultiple days. The dashboard is configured such that the relevantpatient information can be displayed as separate graphs, as combinationsof content on the same graph, and/or with scores or analyzed contentdistilled into metrics according to time (e.g., over a period of time orrange).

FIG. 7K illustrates an “at-a-glance” patient card with patient-reportedand patient-generated data from the sensor. In this embodiment, thepatient's name is displayed with the type of procedure and the name ofthe doctor (e.g., Dr. Monica Anderson) displayed in the upper leftcorner of the card. In the right corner, the card displays the “Time inEpisode,” in this case the user is in the window of greater than 14 dayspost-surgery. The middle section of the card displays the patientreported data (PRO) with the indicated graphical display of paincontrol, as well as post-op Meds, and Symptoms reported by the user. Inthe bottom section of the card, patient generated health data (PGHD)from the sensor is reported in a graphical form as physical therapy (PT)adherence, range of motion (ROM, %) and walking distance. These cardsmay be configured for use by the user (e.g., patient, athlete, etc.) orby a physician or healthcare provider, for example as part of ahealthcare provider interface or portal. In some instances, thephysician or healthcare provider may sort, process, filter, or displaythe cards in a way that is useful to the routine or healthcare providerprotocol procedures, to facilitate timely providing of care.

In various embodiments, an application may comprise one or moreinterfaces or graphical displays configured to encourage and/or sustainuser (e.g., patient, athlete, etc.) engagement using gamification. Theinterface may be configured such that the display of content iscustomized according to the psychological profile of the user. Examplesof customizable features of the display include but are not limited tothe following: marquee icon change color to indicate engagement,adherence, and progress; app's marquee icon animates itself to engagepatient and provoke action; rewards for patients may be provided foraccomplishments such as repetitions completed or number of timessessions were done in a week; leaderboard to motivate users throughcompetition and comparison with others; etc. Techniques may be used andcustomized according to a behavioral profile compiled for the user thatmay facilitate engagement of the user through display of the userinterface such that the user is motivated to stay on the recoveryjourney.

In various embodiments, the application (e.g., monitoring system) may beconfigured to engage, activate, or trigger the participation ofproviders aside from the user (e.g., patient, athlete, etc.) to supportand encourage the patient at critical junctures in the user's care ortraining plan. The engagement or activation of providers outside theuser may be triggered by the Monitoring system in accordance with theuser/patient's psychological profile (e.g., motivated, disciplined,etc.). The application may be configured such that a suggestion orsuggestions may be provided to a caregiver or outside provider at userspecific critical junctions in the care process, with suggestions to thecaregiver or outside provider that are custom tailored to assist thepatient in a way that supports the user's accountability for their care.In further instances, the application may comprise a bot or trainableautomated response system that may engage with the user according to theuser's psychological profile. In further embodiments, bots may beemployed in response to input from a designated caregiver or outsideprovider—for example if the caregiver or provider is unable to attend tothe user's needs in a way (e.g., within a time frame) that is necessaryfor the user (e.g., according to the user's specific needs or profile).In some embodiments, the application may comprise a mechanism forfacilitating communication between the user and an outside provider orcaregiver (e.g., messaging system integrated with functionality of theapplication—including for example various means of communicationincluding audio, picture, video, etc.).

The information requested from, and provided to, the monitoredindividual is customizable based on the intended use of the monitoringsystem. In some embodiments, it is customizable by a systemadministrator. Additionally or alternatively, in some embodiments, it iscustomizable by a healthcare provider, athletic coach, personal trainer,or other health or wellness supervisor.

The health application 622 of various embodiments also includes a dataprocessing module 626. The data processing module 626 includes thesoftware that instructs the mobile computing device 600 to performvarious data processing methods. One method directed by the software ofthe data processing module 626 is depicted in FIG. 8 . As shown at block810 of the depicted method, the mobile computing device 600 receivesmeasurements from the sensor system 300. As described above, thesemeasurements may be relative or absolute measurements. The measurementsinclude circumference measurements of the body portion, positionalmeasurements, orientation measurements, or measurements indicative ofcircumference. In some embodiments, the raw measurements (such asmeasurements of changes in resistance, capacitance, or inductance) arereceived by the mobile computing device 600 from the sensor system 300and processed by the mobile computing device 600 to determinecircumference, position, and/or orientation. Additionally, themeasurements may optionally include one or more additional measurementsof health parameters such as orientation, acceleration, range of motion,skin temperature, skin color, and/or cardiovascular performance (e.g.,blood oxygenation, blood volume, pulse rate, or heart rate). At block820, with the aid of the data processing module 626, the mobilecomputing device 600 processes the received measurements to track andanalyze changes in the body portion. Additionally, the mobile computingdevice 600 determines when to generate an alert output and what alertoutput to generate based on any analyzed changes to the body portion, asshown at block 830. The mobile computing device also transmits data,including the received measurements, the analysis of measurements, anddata received via user inputs, to a network computing device, as shownat block 840.

In some embodiments, processing the received measurements to track andanalyze changes in the body portion involves assigning a relative weightto one or more measured parameters of importance and calculating anoverall score from the weighted measurements, as described elsewhereherein. Additionally, one or more user inputs and/or a compliance scoremay contribute to the overall score.

In some embodiments, the compliance score is also calculated by themobile computing device 600 using the software of the data processingmodule 626. The compliance score is an indication of the degree to whichthe monitored individual complied with prescribed instructions. Thecompliance score may be calculated based on one or more of: the changein circumference of the body portion, the user inputs, detected motionof the body portion indicative of an exercise, a detected orientation ofthe body portion, a range of motion of a body portion, a quantity ofprescribed exercise performed, and a quality of prescribed exerciseperformed. For example, if the prescribed instructions include aninstruction to upwardly tilt or elevate the legs, the compliance scoremay be determined, at least in part, by monitoring leg orientation. Ifthe prescribed instructions include an instruction to perform legexercises, the compliance score may be determined, at least in part, bymonitoring leg movement, and optionally calculating a number and qualityof leg exercises performed. If the prescribed instructions include aninstruction to administer a medication, the compliance score may bedetermined, at least in part, from a user-entered input indicatingmedication administration.

In some embodiments, the mobile computing device 600 using the softwareof the data processing module 626 may apply one or more algorithms forpatient engagement, adherence, and progress tracking. In someembodiments, algorithms for patient engagement may be run on a cloud(e.g., care cloud) or a server that is not located on the device usingdata collected from devices or systems disclosed herein. Patientengagement algorithms may be generated using unsupervised, supervised,or semi-supervised methods. In some instances, patient engagementalgorithms may be generated using neural networks. Patient engagementalgorithms may be generated based on positive performance outcomes, forexample based on parameters that produced positive outcomes fromprevious patients or based on deductions or analysis conducted bymedical professionals.

Monitoring systems as disclosed herein may comprise dynamic progresscharting, such that the progress in a care plan, including towards caregoals (e.g., ROM, pain reduction, etc.), can be measured and adjustedbased on incoming data. The monitoring system may collect hundreds offeatures and inputs that update performance dynamically. In FIG. 13A, anexample of ROM is shown deviating below normal in relation to physicaltherapy (PT), however hundreds of features could be used in addition tothe two illustrated in this example. The monitoring system learns idealprogress from a patient at defined time increments (e.g., every 6 hrs,12 hrs, 24 hrs, 36 hrs, 48 hrs, etc.) from all patients. The monitoringsystem is dynamically adjusted, as shown by the dots of the plot in FIG.13A. The dynamic adjustment performed by the monitoring system exceedsperformance of current medical methods, which apply a fixed/statictrajectory and path of ideal progress to all patients regardless oftheir unique dispositions or conditions, thereby providing a lessaccurate prediction with little if any means for guiding or optimizingcare to the particular patient or user. FIG. 13B and FIG. 13C showexemplary information that may be factored into the prediction shown inFIG. 13A to enrich and better predict and inform the next-best-decisionapproach to generate an adaptive care plan.

In some instances, algorithms may be run as part of the care cloud suchthat the care cloud may perform statistical analysis and generatepredictions for a single user or groups of users based on correlationsbetween the data provided by a given patient/user and the body of dataand models that have been previously generated and have known outcomes.Examples of correlations that may be made as part of the care-cloudanalysis comprise correlations between range of motion and exercise areshown in FIG. 14A.

As shown in FIG. 14A, the application may recognize patterns in thetracking data obtained from the sensor as the sensor monitors the planarmovements of the body across sagittal, transverse, and coronal planes.The sensor tracks all types of movements and measures the degrees offlexion, extension, abduction, and adduction for any exercise prescribedby a healthcare provider (e.g., physical therapist, etc.) and recordsrepetitions of completed exercises against the prescribed total. Thecare cloud captures the repetitions and range of the planar movements ofthe body when the patient does pre-habilitation exercises and creates apoint of reference or baseline for the patient. The sensors detect andrecord the ranges of planar motions of the user/patient post-MSKprocedure and the recordings are transferred to the care cloud where thecare cloud may correlate the repetitions done over a period of time tothe range of motion (ROM) improvements as well as monitor other aspectsof the user's performance including strength and endurance. By comparingpre-habilitation benchmark data with post-operative exercise data, thepatient's recovery against a pre-operative baseline and improvementbeyond a pre-operative baseline or pre-injury baseline may beestablished quantitatively. The care cloud may generate alerts if thereare deviations to the thresholds (e.g., deviations from thresholdsdetermined and set up by a healthcare provider) for the patient-reportedfeedback and patient-generated sensor data. Based on one or more of: thecare plan, the performance of the user, and the thresholds set by thehealthcare provide, the care cloud may trigger the patient throughpersonalized messages to act based on adhering to an exercise protocolin the care plan. The care cloud may also trigger caregivers andsupporting friends or family to provide empathetic support (e.g.,Empathetic Guidance) for the patient in his/her adherence to an exerciseprotocol in the care plan.

The monitoring system, using the care cloud and/or care relatedalgorithms, may identify correlations between pain medication and painlevels for a given user, as illustrated in the example of FIG. 14B. Inthis example, the care cloud generates alerts if the patient-reporteddata on medication adherence and/or pain level exceeds or does not reacha given threshold set up by a health care provider. In furtherembodiments, these alerts may be displayed on a healthcare providerinterface. The monitoring system may, using inputs regarding painmedication and pain level, also alert clinicians if a patient is nottaking mandatory medications such as blood thinners or not takingmedications needed to counter symptoms such as constipation when thosesymptoms are reported by the user/patient. The care cloud may engagewith the patient through personalized messages to act based on thepredication prescribed by the healthcare provider as part of theuser/patient's care plan. The care cloud may also trigger the caregiversor supportive friend/family to get them engaged in supporting thepatient and their adherence to pain management in the care plan.

The monitoring system, using the care cloud and/or care relatedalgorithms, may identify and correlate pain and medication with range ofmotion (ROM) detected by one or more sensors. As shown in FIG. 14C, thecare cloud may correlate the medication adherence to the pain level aswell as exercise adherence and ROM improvements. Absent contraindicatedsymptoms, patients that manage pain well may be exercising as prescribedto achieve clinical outcomes. In some embodiments, deviations from thepredicted progression for a given patient as illustrated in FIG. 14C maycause concern and activate a message to the user/patient's care teamand/or healthcare provider.

The monitoring system, using the care cloud and/or care relatedalgorithms, may perform patient segmentation and comparison to assessand categorize the user/patient, and accordingly generate usefulreminders or psychological support that may facilitate a user/patient'srecovery. As shown in FIG. 14B, the patient may be assessed according tothe likelihood of one or more of a set of risks including, but notlimited to, fall risk, adherence risk, and readmission risk. These risksmay be dynamically assessed according to feedback provided by thesensors as well as input provided directly by the user/patient. Riskstratification may also be performed by the care cloud, for example thecare cloud may process the user/patient's response to a digitized listof questions that enables the healthcare team to determine the patient'spotential risk after a procedure. The care cloud may compute the riskpropensity of the patient and deliver the information to the healthcareteam based on well-known clinical research (e.g., DVT risk, readmissionrisk, etc.) using characteristics of the patient or the patient'shistory. The care cloud delivers the outcomes of the risk stratificationfor the user/patient to the healthcare provider through the healthcareprovider interface.

In various embodiments, patient engagement algorithms may use patientprogress and episode timeline to reinforce adherent behavior for patientand provide actionable insights for healthcare providers. Patientengagement algorithms may result in contextual and personalized messagesto patients that assist in helping them engage with, adhere to, andprogress physically through a care plan.

In various embodiments, an engagement algorithm may be part of an AI(artificial intelligence) engine. As shown in FIG. 14C, the AI enginemay comprise three levels or tuples for providing a context for aparticular user (e.g., patient context), and assessing a correspondingcare regime for said user. A patient context may comprise three elementsor tuples (3-tuple system): time, adherence, and user/patientpersonality type; these three elements may be used to generate acustomized and adaptable care plan for a user. The element of time maycomprise a time in a particular episode or in a given day. The elementof adherence may comprise one of three characteristics: engaged,adhering, and progressing. The exemplary progression of a user'sadherence is shown in FIG. 14D, which illustrates the state for apatient at any time. As illustrated in FIG. 14F, four stages of a userare as follows: a user is connected when the user uses their phone tologin to an app, the user is “engaging” when the user uses the app acertain number of times a week, the user is “adhering” when the useruses the app to report symptoms, pain level, medication taken,exercises, etc. and finally the user is “progressing” when the usershows the improvement in pain level and/or range of motion (ROM). Thethird element or tuple relates to the user/patient personality type, auser may fall in one of three categories: motivated, amenable, and notmotivated.

The tuple at any instance has attributes that allow mapping the patientinto a recovery curve as shown in FIG. 14E. An AI (artificiallyintelligent) model may comprise steps for computing and assessingpatient performance, status, compliance, adherence, engagement,progress, etc. using a recovery curve. In some instances, a recoverycurve may comprise a sigmoidal shape as shown in FIG. 14E, withvariations in pain level and ROM (range of motion) as outputs based oninputs to the 3-tuple system. Each tuple of the 3-tuple system mayrepresent a given feature of the model being generated and the curve(e.g., sigmoidal curve) may represent performance of a particular usersuch that the user may be assessed according to a model represented bythe sigmoidal curve, with thresholds and benchmarks established based onoutcomes or feedback provided by the previous users of similarcharacteristic. In some instances, a clinical outcome of a successfulrecovery may comprise positive (e.g., increasing) measure of range ofmotion and negative (e.g., decreasing levels of pain). For example, apositive outcome may include no pain when knee is flexed from 0 to 135degrees; and no pain when knee is extended from 90 to 0 degrees. Themodel may be constantly refined using additional patient data as morepatient data is accumulated and analyzed. Once a benchmark model is inplace, machine learning classification methods may be used to determineperformance of any given user going through the recovery cycle.Customizations associated with a given user's path through the systemmay be delivered in accordance with the user's performance according tothe model and the 3-tuple system. The user's performance may beconstantly updated and dynamically assessed to adjust and adapt to thecurrent state of the user at any given time. The AI model andclassification based on the AI model may be used in conjunction, or runon and/or with data obtained from the care cloud such that context-based“next-best-action” decisions (e.g., next-best-action decision tree) maybe made at any instant in a particular recovery period.

The monitoring system may use one of the following machine learningmodels: Gradient Boosted decision tree, logistic regression, and supportvector machines. The monitoring system, including components of the carecloud, may comprise one or more algorithms. In some instances, one ormore of the models is initialized with known risk factors such as age,weight, gait, etc. for the patient. In a tree based model, the weightson each branch are initialized such that the tree outputs a probabilityof meeting the next day goal in recovery. The monitoring system mayincorporate a recovery or recuperation model. As the patient goesthrough recovery, each piece of data is fed into the model and the modellearns. The data could be physiological measurements, the adherence tothe exercise protocols, the medical history, social activity of thepatient, as well as answers to questions about feeling or well-being. Ina tree based model, the weights of the tree are constantly adjusted atfrequency intervals. Frequency intervals may comprise durations of lessthan or equal to 24 hours, 16 hours, 8 hours, 4 hours, 2 hours, 1 hour,30 minutes, 15 minutes, 10 minutes, 5 minutes, 1 minute, 30 second, or 1second.

The monitoring system may comprise a method of determining the outcomeof a patient for the entire recovery trajectory of the patient,generating a recovery trajectory that could not otherwise be generatedfrom the simple inputs of initial factors on the day of and the dayafter an operation—as is currently the case in current medicalliterature. The models used in the monitoring system are configured tocollect both PRO and PGHD at intervals and over time periods, andextract information about the progress of the patient, such that thesystem may predict and provide an appropriate path to recovery. Forexample, a patient may begin using the application before a surgicalprocedure. Relevant information may be extracted from the sensors data(PGHD) and the patient provided responses (PRO) enabling the system togenerate a baseline. Notifications may be provided to the user to assistin the preparation for the procedure. After the procedure, the PGHD andPRO data will be collected and compared to the baseline informationcollection from the patient prior to the procedure. Initially, thesystem makes an assessment based on information received by the patient,and over time the initial weights of the system are adapted to thebehavioral data provided by the patient (e.g., the PGHD and PRO) suchthat the initially provided information (e.g., age of the patient, etc.)would be less represented by the weights of the system than theperformance of the patient on the physical therapy exercises and therelative range of motion demonstrated by the patient. Furthermore, themodel may be configured to assess a probability of completing physicaltherapy on or by a particular day, given the trajectory of performanceof the patient and the PGHD and PRO data. The application may performvarious simulations based on adjusting (e.g., increasing/decreasing) oneor more factors (e.g., adherence, performance, compliance, etc.) todetermine the best action for the system that will bring the probabilityof completing physical therapy on or by a particular day closer to 100%.The next best action may differ between patients, and the Monitoringsystem collects data and inputs and customizes the response accordingthe patient and the PGHD and PRO data collected about the patient suchthat the next best action for one patient could be to taper downphysical therapy exercises and, for another patient, the system undersimilar initial conditions (e.g., demographic factors and similarinitial pre-operation and immediately post-operation factors) mayrecommend that the patient performs additional physical therapyexercises. For another patient with similar initial conditions, themonitoring system may suggest that the patient spend more time withtheir family and engage in activities that support and/or encouragethem. The monitoring system is configured such that it may collect andassess physical, behavior, and psychological factors and integrate thesefactors into a patient profile and an ongoing care plan that establishesthem on a customized and adaptable trajectory for progress and recovery.

A next-best-action feature of the monitoring system (e.g., decisionmodel, next-best decision tree, or other machine learning basedalgorithm) moves the patient through a recovery curve by ensuring thatthe user (e.g., patient, athlete, etc.) is in one of a set of statesand/or sub-states at any given time. States that may be included in a“next-best-action” decision tree may comprise: risk stratification,medication adherence, pain management, checklist for preparation forsurgery, customization of checklist to personalized care plan based onpatient profile, education/quick reference library, daily tidbits forpatients, combination of voice-based and touch/vision-based capture ofpatient-reported data, and adaptive goaling to keep patients engagedwhile making progress.

Risk stratification may comprise the delivery of a digitized list ofquestions to the patient, which enable the healthcare team to determinethe patient's potential post-procedure risk. In some instances, the carecloud may collect and store the data, and perform an assessment tocompute the risk propensity of the patient, while also delivering theinformation to the healthcare team. The outcomes of the riskstratification may be used by the healthcare team to create a customizedcare plan for the patient after the procedure.

Medication adherence may comprise one or more features of theapplication, which involve reminding patients to take their medication.The care cloud may collect and store data about the medication adherenceof a particular user, and may enable the healthcare provider tocustomize the medication for each patient. The app may enable the user(e.g., patient) to log the amounts of medication that the user took, andin some embodiments, may enable the user to also record symptomsexperienced that may or may not be related to the medication taken bythe user.

Pain management may comprise one or more features of the application,wherein the application is designed to deliver a holistic painmanagement module that coaches users through simple techniques to managetheir pain. The app may periodically query the users on their level ofpain. Examples of pain management modules that may be activated by auser or healthcare provider may include routines of pain medication,icing of a body portion, elevation of a body portion, and alternativepain management techniques.

Checklists for preparation for surgery may be included in theapplication and provided based on the next-best-action decision tree orcare protocol. The application delivers a personalized checklist to thepatient to enable them to prepare for surgery. A personalized surgerypreparation checklist may comprise medical instructions prescribed bythe healthcare professionals and instructions to prepare their home,pack for the hospital and transportation after the surgical procedure.The checklists may be time based, and provided in multiple steps leadingup to the day of surgery. A user (e.g., patient) may be reminded tocomplete the tasks using reminders provided in the application and askedto check off completed items.

The application may be configured to provide customization of achecklist to personalize a care plan based on a user's (e.g., patient's)profile. Customization may comprise delivering a customized checklistfor symptoms after a procedure. Customization may be performed by one ormore healthcare providers. In some instances, a checklist may be drivenby risk stratification of patients prior to the procedure/treatment. Achecklist may be presented to the patient as part of a curated caresession. In some instances, elements of the checklist are presented tothe patient on a periodic or ongoing basis.

Educational and quick reference materials may be provided to a userthrough the monitoring application. In some instances, the applicationmay maintain a set of content for the patients that are available forthe patient to look up. Content may comprise: “how to” guides to preparebefore surgery, a set of “how to” instructions post-surgery, and adetailed care plan so that users/patients know what to expect.

Relevant and personalized daily tidbits may be delivered via theapplication to keep the user experience fresh and informative. Tidbitsmay comprise education content including: content tailored by what apatient needs to do a better job with, module overviews to set userexpectations, prompts for one-time tasks (e.g., pre-habilitationpreparation, exit survey, etc.), motivational stories (e.g., successfulpatient case studies) with customization that ensure the user is similarto the provided case study, light hearted images/messages when cheer isneed (e.g., customized to when the user may be frustrated by progress orexperiencing pain, etc.), tidbits created by the user's support team(e.g., friend and family members, etc.) including, for example, picturesof the user's grandchildren with motivational messages, trend analysisto deepen self-reflection (e.g., customization may comprise identifyingpositive/negative trends and provide corresponding feedback forself-reflection), and milestone based celebrations (e.g., customizationmay include feedback on positive milestones including driving, usingcane, etc.).

Customization of voice-based and touch/vision-based capture ofpatient-reported data may be used to facilitate interaction between theapplication and the user and healthcare providers. In some instances,the application may be configured to support patients sending picturesof a surgery site to a healthcare provider. In some embodiments, thepatient may send a picture of an exercise being performed to ahealthcare provider and the patient may virtually consult with thehealthcare provider to get feedback. Patients may communicate over audioand/or video with healthcare providers, as well as with their supportteam including family and friend caregivers. In some embodiments, thepatient may respond by tapping on the phone or the sensor and that iscaptured as part of the patient input. A patient may speak to avoice-based system (e.g., amazon Alexa, or Siri, Cortana, etc.) andenable the system to capture input from the user.

Adaptive goals (i.e., adaptive goaling) may be included as part of theapplication to keep users engaged while making progress. In someembodiments, the care cloud may capture the range of the planarmovements of the body when the patient does rehabilitation exercises andmay, from the captured data, create a point of reference or baseline forthe patient. In some instances, the care cloud captures the range of theplanar movements of the body post-MSK procedure. The care cloud maydetermine the range of motion of the patient and adaptively create thenext goals for the user to reach. Adaptive goals enable customizedassessment of the user's performance such that the user/patient isprovided with a care plan comprising achievable goals (e.g., range ofmotion goals) as part of their recovery plan. Adaptive goals preventpatients from getting dissuaded by standardized goals, and thereforedropping off from their care plan routine due to goals they deem to beunachievable—adaptive goals also allow healthcare providers tocontinuously monitor and assess the performance of a user relative topredicted expectations, as a way of determining the overall progressionof the user in their recovery and relative to other similar users thathave been through and/or are going through similar recovery.

Various data-based feedback loops may be involved in gathering data fromthe patient. In some embodiments, the monitoring system may capturepatient data and allow information to be selectively shared by thepatient with an ecosystem of providers using a distributed ledger systemor blockchain. The distributed leger system may provide encryptedprotection on networks such that data is preserved and protected.Providers that may be part of the blockchain include care providers(e.g., hospitals, physicians etc.), insurance providers, software-basedservice providers (e.g., wellness analytics providers, nutritionists,etc.), and dentists (e.g., for prophylactic antibiotic prescriptionsduring dental procedures). A blockchain based system may comprise thefollowing: non-repudiable health records with multiple validated copies,electronically “notarized” records that are authenticated by initiatingservice providers, de-identification of data exposed to ecosystem ofproviders, time access to data with expiry of access (e.g., 30-dayaccess, or 2-week access), always enabling control at the individual onwhen to share what data and with whom, and revoke access at any time todata (e.g., “patient's right to be forgotten”). A data store may be keptin blockchain with delineation between personally identifiable dataelements and not personally identifiable elements.

In some embodiments, as shown in FIGS. 15 and 16 , a quantity orquality, respectively, of the exercise performed may be included in theadherence score or calculated as a separate indication of overallexercise performance. A method 1500/1600 for calculating a quantity orquality of an exercise or a prescribed exercise includes blocks1510/1610, which recite positioning the sensor system on or around abody portion of an individual. At blocks 1520/1620, the method includesreceiving measurements for a plurality of parameters during an exerciseor a prescribed exercise. As described in FIGS. 15 and 16 , the sensorsystem is positioned on or around an arm, leg, torso, ankle, wrist,chest, or any other body portion and a plurality of parameters aremeasured, including, but not limited to, a circumference of the bodyportion, an orientation of the body portion, a movement of the bodyportion, an acceleration of the body portion, or a change in any one ormore of the plurality of parameters. For example, an increase incircumference may indicate flexion and a decrease in circumference mayindicate extension. The methods diverge at blocks 1530 and 1630.

At block 1530 of FIG. 15 , the method 1500 includes calculating a numberof repetitions performed of the exercise. In some embodiments,calculating includes measuring an orientation or movement of the bodyportion over time, during the exercise, and extracting a patternindicative of a repetitious movement. Such a pattern may be furtherprocessed to extract a repeating unit of the pattern indicative of onerepetition. The number of repeating units are summed to determine atotal number of repetitions performed.

At block 1540 of FIG. 15 , the method 1500 includes comparing the numberof repetitions or total number of repetitions performed to a prescribedor target number of repetitions for the exercise. In some embodiments,an individual is notified (e.g., visually, audibly, haptically) when thenumber of repetitions falls below a prescribed number of repetitions; insuch embodiments, a caregiver, friend, family member, peer, and/orhealthcare provider may also be notified. Alternatively or additionally,the user is notified when the number of repetitions exceeds or meets aprescribed number of repetitions. In the case of exceeding theprescribed number, the system may warn the user to not exacerbateexisting injuries or incur new injuries by exceeding the prescribednumber or the system may congratulate the user or provide positivefeedback to the user when the user is trying to increase muscle tone orstrength. The calculations and comparisons may be performed locally inthe sensor system or remotely on a mobile computing device, networkcomputing device, or supervisor computing device.

At block 1550 of FIG. 15 , the method 1500 includes comparing theperformance of one or more repetitions performed on the current day torepetitions performed on previous days. In some embodiments, thecomparison of current performance with previous performance may be usedto compute one or more of the scores (e.g., adherence score, performancescore, overall score, etc.). In additional or alternative embodiments,the performance of the repetitions may be combined with one or moreaspects of user/patients performance or state, including for exampleadherence to medicines, pain level, exercise adherence, and/or range ofmotion.

At block 1630 of FIG. 16 , the method includes calculating a quality ofperformance of an exercise. In some embodiments, calculating includesdetecting one or more of: a body portion orientation, movement,circumferential change, and one or more parameters derived from one ormore of: the detected body portion orientation, movement, andcircumferential change. Calculating may additionally or alternativelyinclude extracting a pattern of movements, positions, orientations,circumferential changes, and additional parameters. For example, if theexercise is a bicep curl, the extracted pattern may include a series offive movements, orientations, and circumferential changes equaling onerepetition repeated over time: (1) full extension of the arm combinedwith a first circumference reading, (2) partial (e.g., 90 degree)flexion combined with a second circumference reading, (3) full flexioncombined with a third circumference reading, (4) partial (e.g., 90degree) flexion combined with a fourth circumference reading, and (5)full extension combined with a fifth circumference reading. In someembodiments, a measure of quality of an exercise is averaged over aseries or set of movements or repetitions, for example, if the user hadhigh quality at the beginning of the exercise set but waning quality atthe end of the exercise set.

In some embodiments, the one or more derived parameters include anundesirable body portion orientation, a circumferential change in aspecific muscle or muscle group, a body portion angle, a speed of theexercise, or a timing of the exercise.

In some embodiments, the one or more derived parameters include anundesirable limb or body portion orientation during the exercise. Forexample, in some exercises, there is a tendency to move the limb or bodyportion in a way that reduces the efficacy of the exercise. Ameasurement of this movement may be used to calculate a quality ofperformance of an exercise. In some embodiments, a limb orientation orbody portion orientation is calculated or presented to the user as apercentage of a maximum value, the maximum value being a preferred valueof the exercise. For example, the lower the measured value of theundesirable body portion orientation, the lower the quality score orquality indicator for performance of the exercise.

In some embodiments, the one or more derived parameters includedetecting flexion or lack of flexion of a limb or body portion. Forexample, as a limb is flexed, a circumference of a limb increases toreflect the flexing of muscles. If the correct muscle or muscle group isbeing flexed during the exercise, a detected change in circumference forthe correct muscle or muscle group of the limb or body portion wouldincrease resulting in an increased quality score or quality indicator.If an incorrect muscle group is being flexed during the exercise, adetected change in the circumference for the correct muscle or musclegroup would be minimal or there would be no detected change incircumference indicating that the exercise is not being done correctlyresulting in a decrease in the quality score or quality indicator.

In some embodiments, the one or more derived parameters include a limbangle achieved as compared to target limb angle for the exercise. Insome such embodiments, the limb angle may be computed or displayed as apercent of a maximum value or as a percentage or value relative to athreshold value. For example, for weight lifting exercises, stretchingexercises, or core exercises, a threshold value may be a time-based goalfor the individual or a target goal for the individual. In suchembodiments, if the individual surpasses the time-based goal or target,the individual has achieved greater strength, flexibility, or range ofmotion thus increasing a quality indicator or score; if the individualfails to reach the threshold, the individual has not met his/herstrength, flexibility, or range of motion goals set by a healthcareprovider or himself/herself thus reducing a quality indicator or score.For exercises in which hyper-flexion or hyper-extension is undesirableor detrimental, surpassing a time-based goal or a target would result ina reduced quality factor or indicator.

In some embodiments, the one or more derived parameters include a speedand/or timing of an exercise. For example, some exercises are not aseffective if they are rushed or some exercises include moving a limb toa specified orientation and maintaining the specified orientation for aprescribed length of time before returning to the original or baselineorientation. In such embodiments, a derived parameter includes an actualholding time versus a desired holding time or an actual repetition timeversus a desired repetition time.

At block 1640 of FIG. 16 , the method includes comparing the quality ofperformance of the exercise to one or more ideal values for the one ormore of: the detected body portion orientation, the detected bodyportion movement, the detected body portion circumferential change, andthe one or more parameters or a template pattern for the exercise. Block1640 functions to determine whether the individual completes arepetition as defined by the system or is only performing a fraction ofthe exercise or is performing the exercise in a sloppy or imprecisemanner. The system may generate a quality score or quality indicatorbased on a difference between the one or more ideal values and theactual detected values or between the template pattern and the actualpattern. In some embodiments, the orientation, movement, circumferentialchange, and one or more parameters are measured over time during theexercise to further determine whether the individual is rushing throughthe exercises or whether the individual is performing the exerciseslowly or intently.

In some embodiments, methods 1500/1600 further include receiving anotification regarding a recommended or prescribed exercise from ahealthcare provider. Alternatively or additionally, methods 1500/1600include receiving a user input indicative of an exercise.

In some embodiments, one or more methods described elsewhere hereininclude comparing the measurements for the plurality of parameters to aset of previous measurements for the plurality of parameters. Suchcomparison allows historical tracking of measurements over time.Further, in some embodiments, the method includes determining a range ofmotion of the body portion or an improvement in the range of motion ofthe body portion over time. In some such embodiments, the methodincludes calculating the range of motion of the body portion bycomparing a first orientation of the body portion to a secondorientation of the body portion. For example, a user may start at arange of motion of 20° (i.e., 0° to 20° in movement) as measured by thesensor system and incrementally increase to 40° (i.e., 0° to 40° inmovement). The method of some embodiments further includesdifferentiating fluid range of motion (i.e., the user reaches 40° from0°) versus stuttering range of motion (i.e., the user starts from 0° andreaches 20° then comes back to 15° and then reaches 40°). The system maydifferentiate between fluid and stuttering range of motion usingmovement measurements, as described elsewhere herein. Fluid range ofmotion is weighted more heavily than stuttering range of motion forpurposes of overall score calculations. Alternatively or additionally,the method includes calculating the range of motion of the body portionby comparing a first orientation of the body portion to a secondorientation of a second body portion. A non-limiting example of suchembodiments includes an individual lying on his/her back on a table andbending his/her knee as far as he/she can while maintaining the footplanted on the table. The angle between the body portion and the secondbody portion is measured to determine a range of motion of the bodyportion.

In some embodiments, the various methods described herein includedetermining a progress of the individual towards a time-based or afuture goal for a range of motion of the body portion. For example, fora time-based goal, if the user adheres to prescribed exercises,prescriptions, and other instructions from the healthcare provider, theuser should reach a pre-determined range of motion in a pre-determinedperiod of time. Such time-based goals may be updated periodically by thesystem as the user reaches or fails to reach various milestones,completes or fails to complete various exercises, or complies with orfails to comply with various instructions given by the healthcareprovider. Further for example, for a future goal, the user should reachfull recovery (e.g., full range of motion or at least recover to his/herrange of motion prior to injury or surgery) in a pre-determined periodof time and/or after completing or adhering to various instructions(e.g., prescribed exercises, prescriptions, etc.) from a healthcareprovider. In some embodiments, determining includes benchmarking therange of motion of the body portion to a previous range of motionreading or to a future range of motion goal or a future time-based goal.The future range of motion goal is based on one or more of: a prescribedexercise, one or more user-initiated range of motion measurements, andtime. In some embodiments, the method includes generating a progressindication for the range of motion of the body portion relative to thetime-based goal, the future range of motion goal, or one or moreprevious range of motion readings. In some embodiments, the methodsdescribed in FIGS. 15-16 are performed locally by the sensor system; inother embodiments, the methods are performed by the mobile computingdevice, network computing device, and/or supervisor computing device.

In some embodiments, an alert output is generated when the overall scoreexceeds a predefined threshold. In some embodiments, an alert output isgenerated when a compliance score falls below a predefined threshold. Insome embodiments, an alert output reporting progress is generatedfollowing every receipt of a parameter measurement. In some embodiments,a supervisor can configure when an alert output is generated. In someembodiments, the alert output is a visual or audible alert presented bythe mobile computing device 600. Additionally or alternatively, thealert output may be a message that is transmitted to anothercommunicatively connected device, such as a supervisor computing deviceor a reviewer computing device.

In some embodiments, the specific data analysis functions performed bythe mobile computing device 600 are customized based on the intendeduse/purpose of the monitoring system. One example of a specializedmethod of data analysis performed by the mobile computing device 600 isprovided in FIG. 9 . In the illustrated example, the mobile computingdevice 600 is communicatively coupled to a sensor system 300 formed ofat least two stretchable components and two sensor modules. A firststretchable component with a first sensor module is positioned on theright leg of the monitored individual and configured to obtain a firstset of parameter measurements, including a first set of circumferencemeasurements. A second stretchable component with a second sensor moduleis positioned on the left leg of the monitored individual and configuredto obtain a second set of parameter measurements, including a second setof circumference measurements. In the illustrated embodiment, processingthe parameter measurements to track and analyze changes includes:comparing the first plurality of circumference measurements to eachother to detect a change in the circumference of the right leg overtime, as shown at block 910; comparing the second plurality ofcircumference measurements to each other to detect a change in thecircumference of the left leg over time, as shown at block 920; andcalculating a difference between the change in circumference of theright leg and the change in circumference of the left leg, as shown atblock 930. The difference between the change in circumference of theright leg and the change in circumference of the left leg may contributeto a determination of the appropriate timing or content of the alertoutput. For example, in some embodiments, an alert output may begenerated when the difference between the change in circumference of theright leg and the change in circumference of the left leg exceeds athreshold value.

Additionally, in some embodiments, the specific data analysis functionsperformed by the mobile computing device 600 may be further customizablefor each monitored individual. In some embodiments, the analysisfunctions stored in software of the data processing module 626 aremodifiable by system administrators and/or health and wellnessprofessionals via interaction with an analytics system stored on anetwork computing device.

Analytics System

A schematic block diagram of the analytics system is illustrated in FIG.17 . The analytics system 1700 is stored on the network computing device130 introduced in FIG. 1C. The analytics system refers to the backendsystem of the overall monitoring system. The analytics system 1700includes a monitored-individual module 1710, a supervisor module 1720, areviewer module 1730, and an administrator module 1740 through whicheach user of the monitoring system can interact with the networkcomputing device 130.

The monitored-individual module 1710 stores all user data related to themonitored individual, including login credentials, medical history, arecord of symptoms, and/or other user-entered information, and a log ofparameter measurements and related analyses. It also stores allinstructions that are transmitted to and downloadable by the mobilecomputing device 600. These include application instructions (i.e.,software) and prescribed health-related instructions intended for themonitored individual. The monitored-individual module 1710 of someembodiments is also configured to perform additional analytics of themonitored individual's data and/or population-wide data. It will also beappreciated by those of skill in the art that, in some embodiments, someof or all the data analysis functions that were described above as beingperformed by the mobile computing device 600 may additionally oralternatively be performed by the analytics system 1700 of the networkcomputing device.

The supervisor module 1720 hosts or stores the software for anapplication-based or web-based supervisor portal, which a supervisor canaccess using a supervisor computing device. Through the portal, a healthor wellness professional can log into the monitoring system and reviewparameter measurements, data analyses, and alerts pertaining to onemonitored individual and/or an entire population of monitoredindividuals. In some embodiments, the portal enables a health orwellness professional to view trends, averages, charts, and otherdisplays of population-wide data pertaining to a plurality of theirpatients, clients, or athletes. The supervisor module 1720 may alsoenable the supervisor to configure and modify alert algorithms, whichthe monitoring system uses to determine when to generate alerts for themonitored individual and what alerts to generate. For example, asupervisor may be able to select which parameters to include in anoverall score calculation and/or what weighting to assign eachparameter. Through the supervisor portal, a health or wellnessprofessional can also create, customize, and/or modify prescribedinstructions for the monitored individual, and select specificparameters for the sensor system to monitor. The supervisor may also beable to select or compose messages for transmission to the mobilecomputing device of the monitored individual.

The reviewer module 1730 hosts or stores the software for anapplication-based or web-based reviewer portal, which a reviewer canaccess using a reviewer computing device. Through the portal, a reviewercan log into the monitoring system and review parameter measurements,data analyses, and alerts pertaining to a monitored individual. In someembodiments, the monitored individual may be able to control whichinformation is shared with and viewable by a reviewer. In someembodiments, the reviewer module enables the reviewer to select orcompose messages for transmission to the mobile computing device 120 ofthe monitored individual and/or the supervisor computing device 140.

The administrator module 1740 includes the software that enables userauthentication of a system administrator. Upon logging into the system,the system administrator may be able to access, and optionally, modify,some of or all the software that forms the analytics system.

The analytics system 1700 connects all the users of the system together,enabling the transmission of information between one or more mobilecomputing devices, one or more supervisor computing devices, one or morereviewer computing devices, and/or one or more administrator computingdevices.

In various embodiments, the network computing device, the supervisorcomputing devices, the reviewer computing devices, and the administratorcomputing devices each includes some of or all the functional componentsdescribed above in relation to the mobile computing device 200 of FIG. 2, but with different software loaded thereon. For example, each deviceincludes a processor and memory having instructions stored thereon,wherein execution of the instructions by the processor, cause theprocessor to perform various methods. Moreover, each of the computingdevices includes a network interface for receiving and transmittingdata, and each computing device may include or be coupled to an inputdevice for receiving user inputs and an output device for conveyinginformation.

The examples and illustrations included herein show, by way ofillustration and not of limitation, specific embodiments in which thesubject matter may be practiced. Other embodiments may be utilized andderived therefrom, such that modifications may be made without departingfrom the scope of this disclosure. This disclosure is intended to coverany and all adaptations or variations of various embodiments, and itwill be readily apparent to those of ordinary skill in the art, in lightof the teachings of these embodiments, that numerous changes andmodifications may be made without departing from the spirit or scope ofthe appended claims.

1.-42. (canceled)
 43. A monitoring system for detecting an improvementin strength and a range of motion of a body portion of a user, themonitoring system comprising: at least one sensor configured to be wornon or around the body portion, the at least one sensor positioned withina stretchable component via a holder of the stretchable component, theholder being configured to maintain a fixed location of the at least onesensor upon the body portion, and wherein the at least one sensor isconfigured to obtain a plurality of measurements that include positionaland orientational assessments of the body portion taken over a period oftime and measured from the fixed location of the at least one sensor onthe body portion during performance of one or more exercises provided aspart of an adaptive care plan; a processor communicatively coupled tothe at least one sensor, the processor comprising instructions storedthereon, wherein the instructions, when executed by the processor, causethe processor to: receive the plurality of measurements, extract apattern from the plurality of measurements, wherein the patterncomprises the range of motion of the body portion and a quality ofperformance of the one or more exercises, compare the pattern to atleast one baseline pattern, determining a change in the range of motionor a change in the quality of performance of the body portion based onthe comparison of the pattern with the at least one baseline pattern,calculate, for the user, a recovery score based on the change in therange of motion or the change in the quality of performance, and furtherbased on the plurality of positional and orientational measurements,wherein the recovery score includes a probability that the user,adhering to the adaptive care plan, will reach a predefined recoverystate within a predefined time period.
 44. The monitoring system ofclaim 43, wherein the predefined recovery state comprises a predefinedmeasure of improvement, occurring over the predefined time period, in atleast one metric associated with the user, the at least one metric beingdefined by a baseline performance of the user and the adaptive careplan.
 45. The monitoring system of claim 44, wherein the at least onemetric comprises one or more of a fatigue level, an adherence score, apain level, the range of motion, a strength level, and a coordination orbalance metric.
 46. The monitoring system of claim 43, wherein theinstructions, when executed by the processor, further cause theprocessor to: receive a user input indicating a level of pain beingexperienced by the user; and adjust the recovery score and thepredefined time period based on the received user input.
 47. Themonitoring system of claim 43, wherein the recovery score furtherincludes a probability that the user, after a surgical procedure, willregain, within the predefined time period, one or more of a strengthlevel of the body portion, a coordination level associated with the bodyportion, and the range of motion associated with the body portion. 48.The monitoring system of claim 43, further comprising causing, based onone or more of the adaptive care plan and the performance of the user inthe one or more exercises, generation of a user interface dashboardincluding one or more of: personalized messages to the user thatfacilitate adherence to the one or more exercises in the adaptive careplan, instructions for the one or more exercises, measurements obtainedfrom the sensors, the recovery score, and a compliance score indicatinga level of compliance by the user to the adaptive care plan.
 49. Themonitoring system of claim 43, further comprising: generating, based onthe measurements, a risk score for the user, the risk score including aprobability generated by weighting the changes, over the period of time,in each of the plurality of measurements, wherein the risk score is arisk for user readmission to a medical facility, a risk of the userfalling, or a risk associated with adherence to the adaptive care plan.50. The monitoring system of claim 43, wherein the recovery scorefurther includes a probability that the user will regain, within thepredefined time period, one or more of a strength level of the bodyportion, a coordination level associated with the body portion, and therange of motion associated with the body portion.
 51. The monitoringsystem of claim 43, wherein the quality of the performance of the one ormore exercises comprises one or more of: a measure of flexibility, ameasure of strength, a measure of endurance, a measure of timing, ameasure of smoothness of movement, a measure of shakiness of movement,positional information, relative fatigue levels, a measure of speed ofmovement, and a combination thereof.
 52. A monitoring system fordetecting an improvement in strength and a range of motion of a bodyportion of a user, the monitoring system comprising: at least one sensorconfigured to be worn on or around the body portion, the at least onesensor positioned within a stretchable component via a holder of thestretchable component, the holder being configured to maintain a fixedlocation of the at least one sensor upon the body portion, and whereinthe at least one sensor is configured to obtain a plurality ofmeasurements of the body portion taken over a period of time and fromthe fixed location of the at least one sensor during performance of oneor more exercises provided as part of an adaptive care plan; a processorcommunicatively coupled to the at least one sensor, the processorcomprising instructions stored thereon, wherein the instructions, whenexecuted by the processor, cause the processor to: receive the pluralityof measurements, extract a pattern from the measurements, wherein thepattern comprises the range of motion of the body portion, compare thepattern to a baseline pattern, determining a change in the range ofmotion of the body portion based on the comparison of the pattern withthe baseline pattern, calculate, for the user, a time-based predictionfor recovery associated with the body portion based at least in part onthe change in the range of motion, the plurality of measurements, andadherence to performance of exercises associated with the adaptive careplan.
 53. The monitoring system of claim 52, wherein the time-basedprediction for recovery is further based on patient reported outcomedata and patient generated health data.
 54. The monitoring system ofclaim 52, wherein a quality of the performance of the one or moreexercises comprises one or more of: a measure of flexibility, a measureof strength, a measure of endurance, a measure of timing, a measure ofsmoothness of movement, a measure of shakiness of movement, positionalinformation, relative fatigue levels, a measure of speed of movement,and a combination thereof.
 55. The monitoring system of claim 52,wherein the fixed location of the body portion comprises a right limb ora left limb of the user, and wherein the processor is further configuredto compare the plurality of measurements to determine a probability ofcomplete recovery of the body portion by a particular time.
 56. Themonitoring system of claim 52, wherein the time-based prediction forrecovery further includes a probability that the user will regain,within a predefined time period, one or more of a strength level of thebody portion, a coordination level associated with the body portion, andthe range of motion associated with the body portion.